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java PhD Projects, Programmes & Scholarships

Python program model-checking/verifying python agents, phd research project.

PhD Research Projects are advertised opportunities to examine a pre-defined topic or answer a stated research question. Some projects may also provide scope for you to propose your own ideas and approaches.

Funded PhD Project (UK Students Only)

This research project has funding attached. It is only available to UK citizens or those who have been resident in the UK for a period of 3 years or more. Some projects, which are funded by charities or by the universities themselves may have more stringent restrictions.

PhD scholarship in data science and robotics

Funded phd project (students worldwide).

This project has funding attached, subject to eligibility criteria. Applications for the project are welcome from all suitably qualified candidates, but its funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

AI-Enhanced Security Fuzzing: Advancing Vulnerability Detection in Software and Hardware Systems

Self-funded phd students only.

This project does not have funding attached. You will need to have your own means of paying fees and living costs and / or seek separate funding from student finance, charities or trusts.

PhD studentship in AI and Collective Intelligence: theory and applications in financial payment and lending cases

Computational methods for medical image analysis: foundation models, generative models and multimodal learning, machine learning for energy system analytics, novel e2e mechanisms for mobile devices, design, implementation and evaluation of non-cooperative game theoretic models for cyber security, digitalisation of energy systems, cyber security, artificial intelligence, machine learning and blockchain technology: mitigating cyber attacks and detecting malicious activities in network traffic, blockchain technology and smart contracts for the security of internet of things and wireless communications, phd in energy systems, data lake exploration with modern artificial intelligence techniques, competition funded phd project (students worldwide).

This project is in competition for funding with other projects. Usually the project which receives the best applicant will be successful. Unsuccessful projects may still go ahead as self-funded opportunities. Applications for the project are welcome from all suitably qualified candidates, but potential funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

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PHD IN JAVA PROGRAMMING 

Phd in java programming always a blooming field as every one of us knows java and ready to work on it. It is not something new to boost upon. Everyone has learn java, it may be due to the curriculum or out of interest. Hence, it is easy to choose topic in this domain as we also know this language. And it is platform independent. Any application can also implement using java, as it works under all environments Java is also a high-level programming language widely used in variety of computing platforms from embedded devices like mobile phones to enterprise servers.

Java applets run in secure, sandbox environments to provide many features of native applications and cab also embedded in HTML pages.

Java programming.

PhD in Java Programming can also list as Approaches and tools for automate End-to-End web testing, Architecture of Semantic Data Quality Management Framework (SDQM, Exploring open EHR-based clinical guidelines in acute stroke care and research Etc. Many PhD projects in java are given below for better understanding.

In reality, we can implement all the fields and research area in java. Many tools are implement using java. Major platform used in every industry is java due to its vary application and easy availability.

It is open source software which can be easily integrate with other platforms. We have best developers in this field. Scholars can choose any field or even own innovative idea; we can implement it within short duration.

Related Search Terms

 java programming research issues, java programming research topics, phd projects in java programming, Research issues in java programming

5 Answers 5

A thread dump is a dump of the stacks of all live threads. Thus useful for analysing what an app is up to at some point in time, and if done at intervals handy in diagnosing some kinds of 'execution' problems (e.g. thread deadlock).

A heap dump is a dump of the state of the Java heap memory. Thus useful for analysing what use of memory an app is making at some point in time so handy in diagnosing some memory issues, and if done at intervals handy in diagnosing memory leaks.

This is what they are in 'raw' terms, and could be provided in many ways. In general used to describe dumped files from JVMs and app servers, and in this form they are a low level tool. Useful if for some reason you can't get anything else, but you will find life easier using decent profiling tool to get similar but easier to dissect info.

With respect to WebSphere a javacore file is a thread dump, albeit with a lot of other info such as locks and loaded classes and some limited memory usage info, and a PHD file is a heap dump.

If you want to read a javacore file you can do so by hand, but there is an IBM tool (BM Thread and Monitor Dump Analyzer) which makes it simpler. If you want to read a heap dump file you need one of many IBM tools: MDD4J or Heap Analyzer.

• 2 MDD4J is deprecated. Memory Analyzer is the recommended tool. –  Chao Commented Jun 6, 2015 at 12:54

JVM head dump is a snapshot of a JVM heap memory in a given time. So its simply a heap representation of JVM. That is the state of the objects.

JVM thread dump is a snapshot of a JVM threads at a given time. So thats what were threads doing at any given time. This is the state of threads. This helps understanding such as locked threads, hanged threads and running threads.

Head dump has more information of java class level information than a thread dump. For example Head dump is good to analyse JVM heap memory issues and OutOfMemoryError errors. JVM head dump is generated automatically when there is something like OutOfMemoryError has taken place. Heap dump can be created manually by killing the process using kill -3 . Generating a heap dump is a intensive computing task, which will probably hang your jvm. so itsn't a methond to use offetenly. Heap can be analysed using tools such as eclipse memory analyser.

Core dump is a os level memory usage of objects. It has more informaiton than a head dump. core dump is not created when we kill a process purposely.

Heap dumps anytime you wish to see what is being held in memory Out-of-memory errors Heap dumps - picture of in memory objects - used for memory analysis Java cores - also known as thread dumps or java dumps, used for viewing the thread activity inside the JVM at a given time. IBM javacores should a lot of additional information besides just the threads and stacks -- used to determine hangs, deadlocks, and reasons for performance degredation System cores

A Thread dump is a dump of all threads's stack traces, i.e. as if each Thread suddenly threw an Exception and printStackTrace'ed that. This is so that you can see what each thread is doing at some specific point, and is for example very good to catch deadlocks.

A heap dump is a "binary dump" of the full memory the JVM is using, and is for example useful if you need to know why you are running out of memory - in the heap dump you could for example see that you have one billion User objects, even though you should only have a thousand, which points to a memory retention problem.

Thread dumps are javacore show snapshot of threads running in JVM, it is useful to debug hang issues, it will provide info about java level dead locks and also IBm version of javacores provides much more useful information, such as heap usage, CPU usage of each thread and overall heap usage along with number of classes laded by the JVM.

Heapdumps, provides information about Java heap usage by an JVM, which can be used to debug memory leaks. Heapdumps are generated by IBM JVMs when a JVM is runs into outofmemoryerror, Heapdumps are only for heap leaks in java, native out of memory error may result system dumps usually with an "GPF" General protection Fault.

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Creating and Analyzing Java Heap Dumps

• March 1, 2021

As Java developers, we are familiar with our applications throwing OutOfMemoryErrors or our server monitoring tools throwing alerts and complaining about high JVM memory utilization.

To investigate memory problems, the JVM Heap Memory is often the first place to look at.

To see this in action, we will first trigger an OutOfMemoryError and then capture a heap dump. We will next analyze this heap dump to identify the potential objects which could be the cause of the memory leak.

Example Code

What is a heap dump.

Whenever we create a Java object by creating an instance of a class, it is always placed in an area known as the heap. Classes of the Java runtime are also created in this heap.

The heap gets created when the JVM starts up. It expands or shrinks during runtime to accommodate the objects created or destroyed in our application.

When the heap becomes full, the garbage collection process is run to collect the objects that are not referenced anymore (i.e. they are not used anymore). More information on memory management can be found in the Oracle docs .

Heap dumps contain a snapshot of all the live objects that are being used by a running Java application on the Java heap. We can obtain detailed information for each object instance, such as the address, type, class name, or size, and whether the instance has references to other objects.

Heap dumps have two formats:

• the classic format, and
• the Portable Heap Dump (PHD) format.

PHD is the default format. The classic format is human-readable since it is in ASCII text, but the PHD format is binary and should be processed by appropriate tools for analysis.

Sample Program to Generate an OutOfMemoryError

To explain the analysis of a heap dump, we will use a simple Java program to generate an OutOfMemoryError :

We keep on allocating the memory by running a for loop until a point is reached, when JVM does not have enough memory to allocate, resulting in an OutOfMemoryError being thrown.

Finding the Root Cause of an OutOfMemoryError

We will now find the cause of this error by doing a heap dump analysis. This is done in two steps:

• Capture the heap dump
• Analyze the heap dump file to locate the suspected reason.

We can capture heap dump in multiple ways. Let us capture the heap dump for our example first with jmap and then by passing a VM argument in the command line.

Generating a Heap Dump on Demand with jmap

jmap is packaged with the JDK and extracts a heap dump to a specified file location.

To generate a heap dump with jmap , we first find the process ID of our running Java program with the jps tool to list down all the running Java processes on our machine:

Next, we run the jmap command to generate the heap dump file:

After running this command the heap dump file with extension hprof is created.

The option live is used to collect only the live objects that still have a reference in the running code. With the live option, a full GC is triggered to sweep away unreachable objects and then dump only the live objects.

Automatically Generating a Heap Dump on OutOfMemoryError s

This option is used to capture a heap dump at the point in time when an OutOfMemoryError occurred. This helps to diagnose the problem because we can see what objects were sitting in memory and what percentage of memory they were occupying right at the time of the OutOfMemoryError .

We will use this option for our example since it will give us more insight into the cause of the crash.

Let us run the program with the VM option HeapDumpOnOutOfMemoryError from the command line or our favorite IDE to generate the heap dump file:

After running our Java program with these VM arguments, we get this output:

As we can see from the output, the heap dump file with the name: hdump.hprof is created when the OutOfMemoryError occurs.

Other Methods of Generating Heap Dumps

Some of the other methods of generating a heap dump are:

jcmd : jcmd is used to send diagnostic command requests to the JVM. It is packaged as part of the JDK. It can be found in the \bin folder of a Java installation.

JVisualVM : Usually, analyzing heap dump takes more memory than the actual heap dump size. This could be problematic if we are trying to analyze a heap dump from a large server on a development machine. JVisualVM provides a live sampling of the Heap memory so it does not eat up the whole memory.

Analyzing the Heap Dump

What we are looking for in a Heap dump is:

• Objects with high memory usage
• Object graph to identify objects of not releasing memory
• Reachable and unreachable objects

Eclipse Memory Analyzer (MAT) is one of the best tools to analyze Java heap dumps. Let us understand the basic concepts of Java heap dump analysis with MAT by analyzing the heap dump file we generated earlier.

We will first start the Memory Analyzer Tool and open the heap dump file. In Eclipse MAT, two types of object sizes are reported:

• Shallow heap size : The shallow heap of an object is its size in the memory
• Retained heap size : Retained heap is the amount of memory that will be freed when an object is garbage collected.

Overview Section in MAT

After opening the heap dump, we will see an overview of the application’s memory usage. The piechart shows the biggest objects by retained size in the overview tab as shown here:

For our application, this information in the overview means if we could dispose of a particular instance of java.lang.Thread we will save 1.7 GB, and almost all of the memory used in this application.

Histogram View

While that might look promising, java.lang.Thread is unlikely to be the real problem here. To get a better insight into what objects currently exist, we will use the Histogram view:

We have filtered the histogram with a regular expression “io.pratik.* " to show only the classes that match the pattern. With this view, we can see the number of live objects: for example, 243 BrandedProduct objects, and 309 Price Objects are alive in the system. We can also see the amount of memory each object is using.

There are two calculations, Shallow Heap and Retained Heap. A shallow heap is the amount of memory consumed by one object. An Object requires 32 (or 64 bits, depending on the architecture) for each reference. Primitives such as integers and longs require 4 or 8 bytes, etc… While this can be interesting, the more useful metric is the Retained Heap.

Retained Heap Size

The retained heap size is computed by adding the size of all the objects in the retained set. A retained set of X is the set of objects which would be removed by the Garbage Collector when X is collected.

The retained heap can be calculated in two different ways, using the quick approximation or the precise retained size:

By calculating the Retained Heap we can now see that io.pratik.ProductGroup is holding the majority of the memory, even though it is only 32 bytes (shallow heap size) by itself. By finding a way to free up this object, we can certainly get our memory problem under control.

Dominator Tree

The dominator tree is used to identify the retained heap. It is produced by the complex object graph generated at runtime and helps to identify the largest memory graphs. An Object X is said to dominate an Object Y if every path from the Root to Y must pass through X.

Looking at the dominator tree for our example, we can see which objects are retained in the memory.

We can see that the ProductGroup object holds the memory instead of the Thread object. We can probably fix the memory problem by releasing objects contained in this object.

Leak Suspects Report

We can also generate a “Leak Suspects Report” to find a suspected big object or set of objects. This report presents the findings on an HTML page and is also saved in a zip file next to the heap dump file.

Due to its smaller size, it is preferable to share the “Leak Suspects Report” report with teams specialized in performing analysis tasks instead of the raw heap dump file.

The report has a pie chart, which gives the size of the suspected objects:

For our example, we have one suspect labeled as “Problem Suspect 1” which is further described with a short description:

Apart from the summary, this report also contains detailed information about the suspects which is accessed by following the “details” link at the bottom of the report:

The detailed information is comprised of :

Shortest paths from GC root to the accumulation point : Here we can see all the classes and fields through which the reference chain is going, which gives a good understanding of how the objects are held. In this report, we can see the reference chain going from the Thread to the ProductGroup object.

Accumulated Objects in Dominator Tree : This gives some information about the content which is accumulated which is a collection of GroceryProduct objects here.

In this post, we introduced the heap dump, which is a snapshot of a Java application’s object memory graph at runtime. To illustrate, we captured the heap dump from a program that threw an OutOfMemoryError at runtime.

We then looked at some of the basic concepts of heap dump analysis with Eclipse Memory Analyzer: large objects, GC roots, shallow vs. retained heap, and dominator tree, all of which together will help us to identify the root cause of specific memory issues.

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1. Introduction

In this tutorial, we’ll explore different ways to capture a heap dump in Java.

A heap dump is a snapshot of all the objects that are in memory in the JVM at a certain moment . They are very useful to troubleshoot memory-leak problems and optimize memory usage in Java applications.

Heap dumps are usually stored in binary format hprof files. We can open and analyze these files using tools like jhat or JVisualVM. Also, for Eclipse users, it’s very common to use MAT .

In the next sections, we’ll go through multiple tools and approaches to generate a heap dump, and we’ll show the main differences between them.

2. JDK Tools

The JDK comes with several tools to capture heap dumps in different ways. All these tools are located under the bin folder inside the JDK home directory . Therefore, we can start them from the command line as long as this directory is included in the system path.

In the next sections, we’ll look at how to use these tools to capture heap dumps.

jmap is a tool to print statistics about memory in a running JVM. We can use it for local or remote processes.

To capture a heap dump using jmap, we need to use the dump option:

Along with that option, we should specify several parameters:

• live : if set, it only prints objects which have active references and discards the ones that are ready to be garbage collected. This parameter is optional.
• format=b : specifies that the dump file will be in binary format. If not set, the result is the same.
• file : the file where the dump will be written to
• pid : id of the Java process

An example would look like:

Remember that we can easily get the pid  of a Java process by using the jps command.

Also, keep in mind that   jmap was introduced in the JDK as an experimental tool and is unsupported. Therefore, in some cases, it may be preferable to use other tools instead.

jcmd is a very complete tool that works by sending command requests to the JVM. We have to use it in the same machine where the Java process is running.

One of its many commands is the  GC.heap_dump . We can use it to get a heap dump just by specifying the pid of the process and the output file path:

We can execute it with the same parameters that we used before:

As with jmap, the dump generated is in binary format.

2.3. JVisualVM

JVisualVM is a tool with a graphical user interface that lets us monitor, troubleshoot, and profile Java applications . The GUI is simple, but very intuitive and easy to use.

One of its many options allows us to capture a heap dump. If we right-click on a Java process and select the “Heap Dump” option, the tool will create a heap dump and open it in a new tab:

Notice that we can find the path of the file created in the “Basic Info”  section.

Starting from JDK 9, Visual VM isn’t included in the Oracle JDK and Open JDK distributions. Therefore, if we’re using anything newer than Java 9, we can get the JVisualVM from the Visual VM open source project site .

3. Capture a Heap Dump Automatically

All the tools we’ve shown in the previous sections are intended to capture heap dumps manually at a specific time. In some cases, we want to get a heap dump when a java.lang.OutOfMemoryError occurs to help us investigate the error.

For these cases, Java provides the HeapDumpOnOutOfMemoryError command-line option, which generates a heap dump when a java.lang.OutOfMemoryError is thrown:

By default, it stores the dump in a  java_pid<pid>.hprof file in the directory where we’re running the application. If we want to specify another file or directory, we can set it in the HeapDumpPath option:

When our application runs out of memory using this option, we’ll be able to find the created file that contains the heap dump in the logs:

In the above example, it was written to the java_pid12587.hprof file.

As we can see, this option is very useful, and there’s no overhead when running an application with this option. Therefore, it’s highly recommended to always use this option, especially in production.

Finally, this option can also be specified at runtime by using the HotSpotDiagnostic MBean . To do this, we can use JConsole and set the HeapDumpOnOutOfMemoryError  VM option to true :

We can find more information about MBeans and JMX in this article .

The last approach we’ll cover in this article is using JMX. We’ll use the  HotSpotDiagnostic MBean that we briefly introduced in the previous section. This MBean provides a dumpHeap method that accepts two parameters:

• outputFile : the path of the file for the dump. This file should have the hprof extension.
• live : if set to true, it dumps only the active objects in memory, as we saw before with jmap.

In the next sections, we’ll show two different ways to invoke this method in order to capture a heap dump.

4.1. JConsole

The easiest way to use the  HotSpotDiagnostic MBean is by using a JMX client, such as JConsole.

If we open  JConsole and connect to a running Java process, we can navigate to the MBeans tab and find the HotSpotDiagnostic under com.sun.management .  In operations, we can find the dumpHeap method that we previously described:

As shown, we just need to introduce the parameters, outputFile and live, into the p0 and p1 text fields in order to perform the dumpHeap operation.

4.2. Programmatic Way

The other way to use the  HotSpotDiagnostic MBean is by invoking it programmatically from Java code.

To do this, we first need to get an MBeanServer instance in order to get an MBean that’s registered in the application. After that, we simply need to get an instance of a  HotSpotDiagnosticMXBean, and call its dumpHeap method .

Let’s see it in code:

Notice that an hprof file cannot be overwritten. Therefore, we should take this into account when creating an application that prints heap dumps. If we fail to do so, we’ll get an exception:

5. Conclusion

In this article, we learned multiple ways to capture a heap dump in Java.

As a rule of thumb, we should always remember to use the HeapDumpOnOutOfMemoryError option when running Java applications. For different purposes, any of the other tools can be used, as long as we keep in mind the unsupported status of jmap.

As always, the full source code of the examples is available over on GitHub .

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PhD Projects in Java

PhD Projects in Java are the  source  of  frequent growth  and  progress  for the PhD fresher. This is the ever widely held medium to build your project using  the topmost coding language . Earlier, we have done so many works using Java project code.

Our Experts will work like JAVA without any dependency on prior work………

Not long ago,  Java  shines as the  celebrating language  amid other extant ones. Right away, it laid the firm impact and gave the benefits in software development. Especially in a research firm, the presence of  Java still remains a considerable part . The future of Java will lift up through specific features. At this moment, check out these areas from the list below.

SUPPORTIVE AND VIBRANT DOMAINS OF JAVA

Cloud computing.

• Mobile-Edge
• Mobile-Cloud
• Green Computing
• And also Cloud-Edge

Embedded System

• Robotics and also in Artificial Intelligence
• Brain Computer Interface
• Sensor Networks
• Computer Vision

Digital Image Processing

• Augmented and also Virtual Reality
• Computer Graphics

Data Mining

• Text and Opinion Abstraction
• Image Processing
• Pattern Recognition  
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DiscoverDataScience.org

Guide to Applying for a Ph.D in Big Data

By Kat Campise, Data Scientist, Ph.D.

Ph.D. programs, in general, are a strenuous undertaking. You’ll spend between 4 to 7 years, on average, in deep and highly structured research on one topic with specific writing requirements. These won’t be blogs or superficial articles waxing poetic about the trials and tribulation of AI. You’ll be expected to publish and present your research to the highest levels of academia who will undoubtedly relish (at least some scholars will) in debating — if not outright challenging — every aspect of the research you conducted.

None of this is meant to scare you away from embarking on the Ph.D. journey. Rather, this is to prepare you for many years of sacrifice and, to be forthright, stress. Ph.D. completion rates hover around 50% . However, this statistic may be more promising depending on the graduate school you choose to attend and the program you intend to complete. For example, Duke University has Ph.D. completion rates as high as 95% .

By the conclusion of your Ph.D., however, you’ll be positioned as one of the leading experts in your chosen area of research. While this doesn’t make you omniscient or omnipotent (too many scholars conflate expertise with being downright arrogant), you will have more knowledge about a given subject than those at the bachelor’s or master’s degree levels. This knowledge is granular, meaning that through your applied research, you will have accrued greater understanding of the nuances involved in the problems you’ve studied at great length.

A Ph.D. is creational. The expectation is that you’ll create or discover something new in your research area. For example, if you’re in the midst of a Ph.D. in Data Science, deriving a brand new AI system, and then discussing how you arrived at this via your dissertation — which you will defend — is what a Ph.D. program will demand of you.

Should You Apply to a Ph.D. Program?

Most Ph.D. programs require full-time study. This will leave very little room for additional employment responsibilities, e.g., having a part-time job or attempting to work full-time. You won’t merely be reading others’ research and then repeating or summarizing it. You’ll critically analyze the strengths and weaknesses of their research, and then use it to inform your research design, development, and implementation. You’re building a brand new solution to a particular problem.

Many Ph.D. programs have a stipulation that you will be part of a teaching cadre, meaning you’ll be teaching either bachelor’s or master’s level students in your discipline. This is in addition to your research and writing. While these may be paid, the teaching assignments don’t tend to be as lucrative as jobs within private industry. For instance, the Bureau of Labor Statistics reports that the median salary for data scientists is $100,910 as of May 2021. It’s extremely unlikely that you’ll earn that type of salary within your Ph.D. in Data Science program.

The flip side of this is that you can reach a six-figure salary once you complete your Ph.D. if you’re willing to take on the opportunity cost during your Ph.D. program. In fact, BLS data says the highest-earning data scientists have salaries of $167,040 or more.

So, should you apply?

If you are certain of the program, which includes having an idea as to what you want to research, you enjoy focusing on a problem (almost endlessly) and creating new solutions, and you’re willing to spend around 6 years of your life constantly reading, analyzing, writing, publishing, and presenting, then start by reviewing the next steps of the application process.

Step 1: Finalize School and Program Choice

Although there are a growing number of online programs, Ph.D. programs are still primarily an onsite experience for the sciences, technology, engineering, and math (STEM) disciplines. So, most Ph.D. applicants will need to take the university location into consideration along with the availability of the specific Ph.D. program.

Regarding program choice, ideally, you should have either a bachelor’s or master’s degree in a related discipline. In many cases, one of the application requirements is for you to have completed specific courses (or a directly relevant degree). Using data science as an example, all Ph.D. programs in data science currently require the completion of Calculus (at the very least, Calculus I), Linear Algebra, and advanced statistics.

Some programs go further and have programming requirements (Python, Java, R, etc.) along with coursework in data structures and algorithms. It’s rare to jump from a B.A. in English to a Ph.D. in Computer Science (or Data Science); not because someone isn’t capable of doing so, but due to the major “catch up” required in terms of extensive practicum in the subject. A Ph.D. is already rigorous without you needing to take a series of prerequisite courses.

Review the current professors’ research interests and publications. One of them is likely to be your advisor and you’ll need to invite others to be a part of your dissertation committee (if the Ph.D. is structured in that way). This will also help you to generate research ideas of your own while also helping your application to “connect” with the department’s goals and objectives.

Additionally, peruse the required courses. If you can find the syllabi for those courses, read through them thoroughly. Note the journals and journal articles they reference. If you can find them (many are locked away in pay for view gateways such as JSTOR, but Google Scholar may have them available for free via PDF), then start reading! Doing so will clue you in on both the professor’s research area — especially if they are an author for one or more of the articles — and the focus of both the particular course and the Ph.D. program.

Remember, the department and its constituents want a high Ph.D. completion rate (which also holds true for master’s and bachelor’s degrees). The prestige factor attracts more students and more students translate into more funding. It’s not all about the money, of course. But, they do strongly prefer candidates who will successfully complete the program and earn their Ph.D.

While you should read the program requirements carefully. Don’t hesitate to gather questions that you can’t find answers to (specifically about the program itself rather than “how do I apply”) and send an email to the Department Chair. Keep in mind that if this is in the middle of a semester, it may take them time to respond to you as they also have teaching, research, and other bureaucratic duties.

Step 2: Review the Application Process

Depending on the department’s website layout, usually, it’s pretty easy to find their “How to Apply” section. Wherever that is located, make sure you find it and review the materials you’ll need to send along with your application. Thus far, just about every U.S. university has an option for applying online (we’ve yet to find one who doesn’t accept online applications). An overwhelming majority of Ph.D. programs require the documents discussed in the steps below. As such, you’ll need to set aside additional time, and money, so that you’ll have all of the requisite materials.

Step 3: Gather Your Transcripts

All U.S. universities are going to ask for official transcripts. During the online application process, you may be asked to upload unofficial transcripts for review by their admission committee. Subsequently, the Graduate Department will request your official transcripts upon admission acceptance. If you have any gaps in education or there was a semester or two where you weren’t performing very well academically, this can be briefly (and professionally) addressed in your Statement of Interest or Letter of Intent; more will be included on this topic below.

Step 4: Test Scores

Some Ph.D. programs are moving away from the GRE testing requirement. Others will accept GMAT test results in lieu of GRE scores. But, STEM programs aren’t likely to abandon the GRE as part of the application process. You’ll need to pay close attention to any cutoff scores listed by the department and whether you should take the General GRE or its Subject Tests .

Depending on where you are located in the world, GRE fees range from $205 to $230 . Subject Tests are $150 per subject. That aside, you’ll also need to spend time in test preparation mode which can be as little as 50 hours and as high as 120 hours. Your test preparation needs are unique and depend on many different factors. Most students perform better on one section over the other, e.g., if you have a Bachelor’s Degree in Math, the Quant section may be a breeze but your performance on the Verbal section may not be as stellar.

Also, keep the application due date in mind when scheduling your GRE test. Give yourself time to retake the test if need be while also ensuring that your test scores are received by the university before the application due date.

Step 5:  Writing Samples, Resumes/CVs, and Letters of Intent

It cannot be overstated that scholarly work at the Ph.D. level requires a mind-numbing amount of writing (and research!). The department admission committee wants to determine if you can write at an academic level and if you have begun to form research interests. Essentially, they want to understand why you want to enter the Ph.D. program and how your studies will align with your career goals. All of this is part of determining not only your commitment but also your readiness.

Having industry experience is a bonus which is one of the reasons they ask for a resume or CV. As much as a Ph.D. seems to be “ivory tower” pontificating — admittedly, it can be —  students who have some hands-on experience in the particular research area tend to have more successful outcomes — as do students who have a set of clear goals and objectives.

If you don’t have an academic writing sample, then this is the time to reach out to the Department Chair to determine what you should write about for application purposes. If you’ve completed a master’s degree, you should have your thesis to send. Some departments will explicitly state what the writing sample should contain. Summarily, if for some reason you don’t have a sample readily available, be prepared to create one.

What the department committee is likely not seeking is for you to have an already formed dissertation topic. If they’re seasoned academics, as they should be, they’re keenly aware that research interests evolve over time. But, as long as you have some direction, e.g., “I’m interested in researching how AI facial recognition can be accurately and equitably deployed in determining the likelihood of criminal activity”, then you’ll have a higher probability of making it to the acceptance pile.

Step 6: Letters of Recommendation

Sometimes referred to as “Letters of Reference” department requirements vary on the number and type of recommendation letters to include with your application. Usually, you’re required to send 3. Since you’re applying for admission into academia, recommendations from prior professors are the prevailing preference. However, an increasing number of universities also accept references from employers if they can include how your employment experience has prepared you for your intended academic studies.

The “how” of routing the reference or recommendation letters differs between universities. Some will still require that the letters are sent via postal mail directly to the department. But, there’s a shift towards simply uploading the letters as a PDF directly to your online graduate application.

Remember Self Care

Your application is viewed from a holistic perspective. Although GRE scores can be part of the admission consideration equation, most universities don’t view you as merely a test score number (which is one reason some are foregoing that requirement). As mentioned elsewhere, the department does want a high graduation rate along with generating scholars who are well-regarded in their expertise. The department admission committees are aware of the blood, sweat, and tears that committing to a Ph.D. program requires.

There is a high probability that you’ll experience disorienting moments including imposter syndrome. Life doesn’t always flow smoothly and definitely doesn’t stop just because you’re in the middle of your Ph.D. in Statistics (or whichever discipline you’ve chosen). It’s perfectly feasible to speak with your advisor about taking a short break from your studies so you can enact self-care. Only you can know and determine if that’s an action (or inaction) you need to take so you can return to your program revived and ready for the next set of challenges.

2021 US Bureau of Labor Statistics salary and employment figures for data scientists reflect national data, not school-specific information. Conditions in your area may vary. Data accessed January 2023.

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GoToppr offers research implementation services the scholars doing research. Our research implementation experts on Java architects draw on a well-crafted set of patterns that makes the integration of several solutions in Java development. We offer services starting from the data collection stage to the implementation stage through planning and design help. Our Java development experts help the scholars to execute the entire process of research implementation through an online help service.

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Our online research implementation team helps the scholars to offer the services utilizing the Java/J2EE technology. Our expert team explains the necessary information regarding the project implementation and assists to develop the coding and provides the latest modification and updates of the software. We also provide the best solution to the queries to solve the issues concerning the research program development and JAVA implementation.

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We also provide research implementation help services for in-demand roles as per the scholar’s requirement. Our expert team of developers provides hassle-free service and guidance to scholars need. Besides, our experts deliver the implementation service as per the scholar’s requirements through innovative ideas and technologies. We guarantee high-quality project delivery with customer satisfaction.

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End-to-end automated web testing JAVA tools utilized for the Ph.D. research that can be integrated easily with other platforms. All the research can be performed in the Java tools because of its easy implementation , availability, and independency. Java uses a high-level programming language that can be utilized in all environments by embedding it with smartphones to servers in a secure way. Besides, the real time Ph.D. research topics such as Intrusion Detection, Machine Learning, Cryptography and Security Issues, Image Processing, Distribution Networks, Data Mining, Cloud Computing, Cluster Computing, Wireless Sensor Networks, Wireless Communication, Grid Computing, and several other types of research domains, Java programming plays a significant role in implementation.

Benefits of using JAVA implementation and JAVA tools

The benefits of Java for Ph.D. research programming developers are:

• Java programming offers a better performance rate and hence applicable for dynamic applications.
• By considering the usage time, the object data available in the system can be removed or stored to offer the automatic memory management service.
• Multi core collection of Java offered through the concurrent package provides the lock-free implementation and code analysis.
• Java programming offers the portable code running as it can be run on any hardware that supports the byte codes and virtual machines due to its portable nature.
• As an object-oriented programming language, the codes will be written in the classes and these classes are considered as an object in Java programming.

Java programming language is utilized at the cloud level to obtain the scalable for developing the applications through Java EE. We offer web solutions for the real time Ph.D. research implementation using Java by our professional online service team experts.

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Numerical Methods Using Java

For Data Science, Analysis, and Engineering

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Implement numerical algorithms in Java using NM Dev, an object-oriented and high-performance programming library for mathematics.You’ll see how it can help you easily create a solution for your complex engineering problem by quickly putting together classes.

Numerical Methods Using Java covers a wide range of topics, including chapters on linear algebra, root finding, curve fitting, differentiation and integration, solving differential equations, random numbers and simulation, a whole suite of unconstrained and constrained optimization algorithms, statistics, regression and time series analysis. The mathematical concepts behind the algorithms are clearly explained, with plenty of code examples and illustrations to help even beginners get started. 

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• Program in Java using a high-performance numerical library
• Learn the mathematics for a wide range of numerical computing algorithms
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Programmers, data scientists, and analysts with prior experience with programming in any language, especially Java. 

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Table of contents (15 chapters)

Front matter, introduction to numerical methods in java.

Haksun Li, PhD

Linear Algebra

Finding roots of equations, finding roots of system of equations, curve fitting and interpolation, numerical differentiation and integration, ordinary differential equations, partial differential equations, unconstrained optimization, constrained optimization, basic statistics, random numbers and simulation, linear regression, time-series analysis, back matter, authors and affiliations, about the author, bibliographic information.

Book Title : Numerical Methods Using Java

Book Subtitle : For Data Science, Analysis, and Engineering

Authors : Haksun Li, PhD

DOI : https://doi.org/10.1007/978-1-4842-6797-4

Publisher : Apress Berkeley, CA

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Softcover ISBN : 978-1-4842-6796-7 Published: 06 January 2022

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Topics : Professional Computing , Big Data

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Operators are used to perform operations on variables and values.

In the example below, we use the + operator to add together two values:

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Although the + operator is often used to add together two values, like in the example above, it can also be used to add together a variable and a value, or a variable and another variable:

Java divides the operators into the following groups:

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Arithmetic Operators

Arithmetic operators are used to perform common mathematical operations.

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Assignment operators are used to assign values to variables.

In the example below, we use the assignment operator ( = ) to assign the value 10 to a variable called x :

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A list of all assignment operators:

Java Comparison Operators

Comparison operators are used to compare two values (or variables). This is important in programming, because it helps us to find answers and make decisions.

The return value of a comparison is either true or false . These values are known as Boolean values , and you will learn more about them in the Booleans and If..Else chapter.

In the following example, we use the greater than operator ( > ) to find out if 5 is greater than 3:

Java Logical Operators

You can also test for true or false values with logical operators.

Logical operators are used to determine the logic between variables or values:

Java Bitwise Operators

Bitwise operators are used to perform binary logic with the bits of an integer or long integer.

Note: The Bitwise examples above use 4-bit unsigned examples, but Java uses 32-bit signed integers and 64-bit signed long integers. Because of this, in Java, ~5 will not return 10. It will return -6. ~00000000000000000000000000000101 will return 11111111111111111111111111111010

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Math pow() method in Java with Example

The java.lang.Math .pow() is used to calculate a number raise to the power of some other number. This function accepts two parameters and returns the value of first parameter raised to the second parameter. There are some special cases as listed below:

• If the second parameter is positive or negative zero then the result will be 1.0.
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getting a job with a PhD in (pure) mathematics

Probably this concern has been addressed several times here but I will give a try to get other people's perspective from my situation. I am feeling sort of hopeless these times.

I am in my last year of my PhD in mathematics and doing my dissertation soon, my research was mainly devoted to algebraic topology and I made some progress in my advisor's particular area. However, I faced some problems during my studies (personal issues) and I never built a strong relationship with my advisor; so I could not finish all the initial research proposal intended for my PhD thesis.

I recently had a sincere talk with my advisor about my future, and he said that my progress is worth for a PhD thesis but it is not anything very surprising that is worth publishing, so I will be graduating with zero publications. He said that my chances of getting a PostDoc positions are really low and that I can not expect a strong recommendation letter from him. My advisor has a few collaborators (from overseas mainly) and none in the local department. He also never supervised another Master or PhD student while I was under his supervision, then I never got a chance to network or engage properly with other classmates. My advisor kindly suggested to explore other options in the job-market besides academia.

I like to explore new things, and in my free time during my studies I attended several teaching workshops, seminars and courses (I got a small degree in "university teaching"), and I am also familiar with Python, R, Matlab and Sage; however, I learn those things for fun and I have never really worked in a big project using any of that.

I do not know what to focus on and what kind of job should I look for after I finish my PhD

I would like to keep doing research in Math, but my area is not very popular lately and I feel that I have no chances of succeed getting a PostDoc; I also feel that I do not have (demonstrable) skills to perform a job with "real-life" applications so probably my chances getting there are also low. And I do not really know the Teaching job-market, I think that those positions are temporary and it would be hard to find a place that is willing to sponsor a work visa (I am not a US-citizen, and the job market in the third-world country that I am from is nonexistent for PhD's in this area)

• mathematics
• career-path

• 4 Learn to program. –  The Dude Commented Jan 25, 2021 at 15:51
• 1 Probably much too late for the original poster, but maybe helpful to some others: have you heard of topological data analysis ? Not saying being good at TDA would get you a job on its own, but could be a stepping stone to applied areas if you already know algebraic topology. –  J W Commented Sep 1, 2021 at 14:06

4 Answers 4

Your supervisor is honest, blunt, and very helpful because by ruling out the academic path he's saving you a lot of time.

The bad news is it sounds like you screwed up your PhD. Not getting strong research results, not having diversified your PhD experience (e.g. lack of teaching experience makes it harder for you to get a teaching job), and not having a firm idea of what you want to do after the PhD means you sound lost right now. The good news is, chances are you learned more than you think you did, and those things are applicable to the job market.

First : don't think you have to do research. It is what you've been doing for the PhD, but it's not what you have to do in the future. Just consider this: if you like research + are good at it, why didn't you do better in the PhD? Why weren't you able to complete the initial research proposals, why weren't you able to get publishable results, why weren't you able to impress your supervisor such that he can write a strong letter of recommendation? You might have good reasons for these, and it's up to you to convince yourself that yes, you still want to do research, and yes, you are talented + motivated enough to succeed in it.

If I were you, I'd start examining the possibility that I am not actually good at research and / or it is not what I want to do. Do some serious soul-searching here. You are making a life-altering decision. If it makes you feel bad that by leaving academia you are "failing", don't worry too much about it: there's a good chance that by leaving academia you'll have a more successful life (in terms of material possessions).

Second : go to your local jobs portal (use Google if you don't know what these are) and search for jobs that require a PhD in mathematics. Do you find anything that catches your fancy? For example searching on indeed.com for "phd math", I get this job among many others. Note the requirements:

Preferred qualifications: MBA, Master's or PhD degree in a quantitative field . Experience with stakeholder management and ability to influence senior stakeholders. Demonstrated knowledge of statistics and data analysis including R programming or other statistical software packages.

I highlighted the most relevant parts. You say you are familiar with R. That means you are in business! You can potentially do this job! If you further have experience with statistics and data analysis (do you?) you're in an even better position.

It's up to you to search the jobs portal for jobs you can do, and then it's up to you to apply. It's true that visa issues might sink your candidacy, but it's also true that because PhDs are relatively rare, you might have skills nobody else will have and therefore the employer is willing to sponsor you. You will not know unless you try.

Do remember that even if you can't find a US employer, you can still work elsewhere. It's a big world out there, and it's not true that third-world countries don't need your skills. Example of such a job in India (admittedly they're not looking for PhD-level candidates, but you could still apply).

Third : once you have an idea about what the options in industry are like, then you can start thinking about what you want to do. Are you sure you still want to do math research? If so I'd talk to your supervisor again about what options are available to you. He's already told you your prospects are not good, so you'll need all the help you can get from him. For example, perhaps you could find a position as a teaching assistant somewhere and do research in your spare time. This will not be easy - doing research while holding a full-time job is very difficult - and it's not likely to be well-paid, but if it's what you want to do, you can try.

On the other hand if you think industry will serve you better, then finish up your thesis & defense and start applying for jobs. Be sure to visit your university's career center as well; they'll be able to help you a lot.

This question is a year old, but it matched my own experience enough (including the specialization in algebraic topology) that I felt obliged to comment. Getting an academic math position is a crapshoot under the best of conditions. The field is glutted with other highly competent applicants (pretty much everyone going into pure math does so with the intention of doing pure math research); the field doesn't have the money of, say, computer science; and there really aren't any opportunites to do pure math research outside of academia and maybe a single-digit number of industry labs. With a great publication record and glowing recommendations from well-established professors, you have an outside shot at it. Without them, it's vanishingly likely to happen. It's unfair, especially if you wind up with a useless or abusive advisor, fall into a field that isn't the cool new thing, choose (or get saddled with) a research project in your limited time to prove yourself that doesn't work out, and so on. What's even worse is that (unlike some other fields) there's really no way to burnish your credentials; there's nothing you can do outside of academia that academic mathematicians would care about.

So, unfortunately, there's probably nothing you can do. It should be relatively easy for you to find some job in industry, though; the tricky part is finding one you like. Just being able to think rigorously and scientifically is a prized skill in industry, while it's taken as given in academia. (Conversely, some interpersonal and organizational skills work the opposite way. On the other hand, there are more opportunities to pick up the latter, whereas there's really no place to pick up pure math skills outside academia.) I don't see anything in your post to suggest that you aren't employable, so at least you should have some breathing room to take a look at what's out there and see if there's anything that might be, if not exciting, at least acceptable.

While a position at a top research institution may not be in your immediate future there are things you can do, up to and including proving that your advisor is wrong.

You don't actually need a faculty position to do research or publish in math but, in my experience, you do need a circle of collaborators. You indicate that you don't have that, but it would be good to start to develop it. Attendance at conferences is a good way in computer science, at least. Meet people, speak with them about ideas, and such.

There are also many teaching colleges around the world that value good math teaching over research. Almost all will require some, but many emphasize teaching the next generation over serious research. The MAA, for example, is full of people who, while doing research, focus much of their efforts on teaching.

And, of course, teaching at pre-college level is also open to you. You may find yourself overqualified for this, but it can make a satisfying life if you value teaching.

But, you can put it together if you have the willingness to do so. A lower level position, while developing mathematical ideas within a circle of collaborators can put you on a path to a better position, proving your advisor wrong in the long term if not immediately.

If your advisor is actually inexperienced at this he may be making a mistake in his analysis and also in his general advising. In particular, his assessment that you can't publish what you've done might just be wrong. You won't know unless you try.

Your life is what you make it.

• 9 Unfortunately, the visa issue is a serious one for US jobs. Academic positions at colleges and universities are not subject to the H-1B quota, but pre-college teaching and industry jobs are, and, even if one can get a position, one has less than 50% chance of getting an H-1B and being able to accept the position. –  Alexander Woo Commented Mar 1, 2019 at 21:42
• 2 The OP should be aware of the "Optional Practical Training" program that allows recent graduates in STEM fields to work in the US for a period of time (up to 3 years as I recall) after completing their degrees. Teaching at a community college on OPT is sometimes a viable option for new PhD's who have acquired enough teaching experience to obtain such a position. –  Brian Borchers Commented Mar 2, 2019 at 0:25

I know it's too late but for someone in the future, there are some folks in machine learning / AI (or more generally applied/engineering) community who are interested in the intersection with pure math, e.g., Topological data science employing the concept of Homology from Algebraic topology or homotopy methods in computational aspects of polynomials.

So, considering these directions and finding positions in industry might be interesting! ** In these directions, people likely do not appreciate the advancement in theoretical direction like the way you used to research on though...

• 3 Forks or folks? –  Moishe Kohan Commented Aug 12, 2023 at 17:21
• 1 @MoisheKohan thank you! –  Rowing0914 Commented Aug 12, 2023 at 21:56

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