phd nus engineering

PhD (by Research): Admission Requirements

Introduction, eligibility for application, other admission requirements.

Doctor of Philosophy (Ph.D.) is a research-based programme and is available in the following departments in the School of Computing:

• Department of Computer Science
• Department of Information Systems

The normal minimum entry qualifications for admission to the programme by research are a good relevant bachelor’s degree/master’s degree. Candidates applying for  Research Scholarships  must have obtained a bachelor’s degree with at least a 2nd Class Upper Honours, or its equivalent in Computing or related discipline. Candidates should have the ability to pursue research in the candidate’s proposed field of advanced study.

TOEFL/ IELTS International applicants* are to submit TOEFL/ IELTS test scores as evidence which demonstrates their academic language ability and readiness for graduate study. Candidates may be required to sit for other tests as prescribed by the School. 

GRE Applicants are encouraged to submit their GRE scores. However, strong applicants who provide sufficient other evidence of excellence may apply without submitting GRE scores, as the requirement can be waived in suitable cases. Candidates who have graduated from Indian universities may submit GATE scores instead.

The minimum TOEFL scores for Internet Based Test (IBT) is 90; or IELTS score of 6.0. GRE and TOEFL scores will be valid for 5 years and 2 years respectively.

*International applicants who graduated from universities where English is not the main medium of communication must submit TOEFL ( http://www.ets.org/ ) or IELTS (International English Language Testing System) ( http://www.ielts.org ) scores.

Submission of Test Scores

Applicants can either:

• submit scoresheets certified by Universities with an original stamp; or
• request the NUS Department to certify their scoresheets if they are in Singapore; or
• request Educational Testing Services (ETS) to send original TOEFL and GRE scores to NUS.

Overseas Interview

Referee's reports.

Recommendations from two academic referees are required for the application. Please use the referee report forms available online If referees are submitting hardcopy letter, it should be duly signed and place in a sealed envelope whos openings bear their signature.

Click here for PhD Online Application.

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Mechanical Engineering

• Graduate study in Mechanical Engineering
• Ph.D. programs

Ph.D. in Mechanical Engineering

The Doctor of Philosophy in Mechanical Engineering prepares students for careers in research and academia. Our collaborative faculty are investigating a diverse range of research areas like additive manufacturing, air quality, cellular biomechanics, computational design, DNA origami, energy conversion and storage, nanoscale manufacturing, soft robotics, transdermal drug delivery, transport phenomena, machine learning, and artificial intelligence.

Interested? Visit our research pages for more information, including faculty areas of expertise and research videos.

• Other Ph.D. programs

I’d like more information.

View the  degree requirements  in the handbook.

Doctor of Philosophy in Mechanical Engineering

Students typically complete the Ph.D. degree requirements in three to five years. Early in the program, students focus on course-work that enhances their knowledge as they prepare to conduct research.

Within one year, students must pass the departmental qualifying exam, an oral exam that tests research skills and knowledge of a core mechanical engineering subject area.

Student research forms the core of the Ph.D. program. Research involves active student-directed inquiry into an engineering problem, culminating in a written thesis and oral defense.

Ph.D. Financial Support

The majority of full-time Ph.D. students accepted through the standard application process receive fellowships that cover full tuition, the technology fee, and a stipend for living expenses for up to five years, as long as sufficient progress is made toward degree completion. These awards are sufficient to cover all expenses for the year (including summers). Students are required to pay for health insurance, the transportation fee, the activity fee, books, and course supplies. Off-campus housing is available within walking distance of campus. At least one year of residency is required for the Ph.D. We offer two ways to enter the Ph.D. program.

Advanced entry Ph.D.

The advanced entry Ph.D. is for students with an M.S. in an engineering discipline or equivalent field.

Direct Ph.D.

The direct Ph.D. is for students entering the program with a B.S. in an engineering discipline or equivalent field.

For a comprehensive overview of the programs, including degree requirements, please consult the most recent handbook

Ph.D. candidate Remesh Shrestha, co-advised by Professors Sheng Shen and Maarten de Boer, explains his research to create polymer nanowires that have high thermal conductivity:

Other Ph.D. programs and partnerships

Apply here (by these deadlines).

For spring 2023

For fall 2022

The application for fall entry opens in October.

More information

Ph.D. employment stats

Ph.D. enrollment and completion stats [pdf]

Teaching Connections

Advancing discussions about teaching, how to make authentic exams in the age of ai.

Olivier Patrick LEFEBVRE

Department of Civil and Environmental Engineering, College of Design and Engineering (CDE)

Editor’s Note: Olivier ponders the impact of generative artificial intelligence (GenAI) on teaching and learning, particularly its impact on the designing and implementation of authentic assessments and the challenges presented to educators. This reflection follows from what Olivier shared at HECC 2023 under the sub-theme “AI and Education”.

 Image by freepik

I have had an interest in creating authentic learning environments—defined as teaching in conditions that mimic the real environment in which the students will apply their knowledge (Herrington & Oliver, 2009)–since I attended a workshop on that topic (Soong, 2015). One of the lessons from that session was the importance of setting authentic assessments as one of the nine elements of a true authentic learning environment (Herrington & Oliver, 2009). So, I started doing just that in my final exams, for example by asking students taking my course ESE5205 “Sludge and Solid Waste Management” to find creative solutions to our waste crisis that align with the national agenda on zero waste (Oh, 2019). Such exams proved particularly valuable when the COVID-19 pandemic hit us, as I found these assessments to be very robust when being taken from home as open-book exams with students having full access to the internet. Indeed, the use of the internet was not only a non-issue for me but in fact, I wanted students to browse the web and learn to differentiate relevant and valuable information that pertains to the course from all the incoherent (or sometimes plain wrong) facts that abound online. I also found it a valuable experience for my students to learn to cite their sources and avoid committing plagiarism, the act of claiming someone else’s work as your own.

And this is where generative artificial intelligence (GenAI) comes in as the new obstacle. Preventing the use of GenAI tools (e.g. ChatGPT) in teaching and learning feels like a step back, especially in designing authentic assessments. In the real world, we do have access to GenAI tools, so an authentic assessment would have to be designed with considerations of allowing students to access it too. Again, I was confident that GenAI would not have the capacity to answer questions that require higher order thinking skills, such as delivering a critical analysis of a report, interpreting a graph, or solving complex problems. So, I did the test and compared the performance of ChatGPT to that of my ESE2001 “Environmental Challenges in the Anthropocene” and ESE5001 “Environmental Engineering Principles” students in their final exam. Are you wondering what the outcome was? ChatGPT performed like an “A student”. It did not just do very well, it also answered questions much faster than I would have been able to do so.

How to solve this issue then? I do not have the answer, especially when we consider the pace at which GenAI is progressing, where the gap between its advances and our capability to even comprehend its implications is growing exponentially (see an illustration in Figure 1).

It seems like an impossible race to try and beat AI at its own game, and raising the difficulty of exams in a desperate attempt to try and “safe proof” them would end up leaving a large number of our students behind and feeling discouraged, as they too would likely be unable to solve them.

For now, all I can think of is to have a mixed environment. For example, the exam could be divided into two steps:

• Step 1 would be a preparation to the exam, consisting of an analysis of the problem at stake with unlimited access to Internet (and GenAI), but without the exact list of questions to be answered.
• Step 2 would then follow where the specific questions about the problem at stake would be given to the students, which they must now answer on their own, without access to the internet, and in their own words .

Meanwhile, I am aware that other teachers are evolving their practice towards making use of AI in their exams. Even though this may sound like the ultimate solution to the problem and definitely authentic, I am concerned that our high-performing, or “A students”, may become the ones that are able to give the best prompts to GenAI (or even worse the ones who can afford the most advanced version of ChatGPT), and are able to optimise making use of AI compared to their peers who may not perform as well academically. Some will argue that the ability to give prompts is the new skill of the future and therefore it is our duty as teachers to train our students for it. One thing is for sure, it is near impossible at this stage for most of us to envision how AI will impact our teaching; nonetheless, it truly is already a revolution that we, as teachers, cannot ignore.

Herrington, J., Reeves, T. C., & Oliver, R. (2009). A guide to authentic e-learning . Routledge.

Hogarth, I. (2023, April 13). We must slow down the race to God-like AI. FT Magazine . https://www.ft.com/content/03895dc4-a3b7-481e-95cc-336a524f2ac2

Oh, T. (2019, August 30). Singapore aims to cut daily waste sent to landfill by 30% by 2030 to extend Semakau’s lifespan. Today . https://www.todayonline.com/singapore/singapore-aims-cut-daily-waste-sent-landfill-30-2030-extend-semakaus-lifespan

Soong, A. S. K. (2015, November 4). Designing an authentic learning environment [Workshop presentation]. Centre for Development of Teaching and Learning (CDTL), National University of Singapore.

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PhD Interview Notice (2024-25)

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