OPENINGS

Post-Doctoral Fellow & Research Assistants (Statistics for Nanoscience)

Job Description – The growth of nanostructures (nanowires, nanotubes and nanodots) using the self-assembly process has gained a lot of momentum in the last few years, for widespread applications in electronics, sensing etc. While it is cost-effective and easy to grow these nanostructures on various substrates, the pattern of growth is not spatially well distributed causing large deviations in the size, density, spacing and alignment of these structures. Statistics and statistical tools can come in very handy in helping to optimize the process conditions for growth so as to minimize the variations observed in the patterns. This project is dedicated to the use of Bayesian statistical models for process optimization of nanostructure nucleation and growth.  Specifically, the work will involve the use of Bayesian hierarchical modeling as well as Kinetic Monte Carlo (KMC) techniques for process simulations.

Job Requirements – Positions are immediately available for talented candidates with background in Statistics. The project will require a sound knowledge of probability and statistics theories and it is preferable that the candidate has some knowledge of applied physics or interest in applying his / her statistical knowledge to the field of nanotechnology.

Candidates should send in their resume / CV along with PDF copies of their relevant publications and scanned copies of their degree transcripts, course grades and certificates (B.Eng, M.Eng and/or Ph.D.) by email (nagarajan@sutd.edu.sg) to Asst. Prof. Nagarajan Raghavan with a subject title – “Application for Post-Doc / RA Position on Statistical Modeling for Nanoscience“.

Suitable candidates will be contacted for Skype Interview. Positions begin immediately and remain open until the right candidate is found. This post-doc position is available for a period of 24-36 months.

Post-Doctoral Fellow & Research Assistants (Prognostics & Health Management – PHM)

Job Description – With systems in the industry getting increasingly complex and with continued evolution of technology in terms of new materials, structures and process technologies, it is hard to fully understand all the wear-out and failure mechanisms inherent in a new technology prior to commercialization for practical use. For such instances, it is essential to be able to “diagnose” the “health” of a system in real-time from sensor data and use this data to “prognose” or “predict” on when the system is likely to behave abnormally in the future based on certain user defined threshold for acceptable performance. This calls for the need to develop advanced statistical algorithms and / or machine learning techniques that can be used to develop a predictive model which works well for practical scenario under non-ideal conditions where there could be multiple failure modes / mechanisms playing a role at the same time, with correlated failures, varying environmental and operating stress loads etc…

Job Requirements – Positions are immediately available for talented candidates with a strong background in Statistics / Machine Learning. The project will require a sound knowledge of probability and statistics theories, in specific, Bayesian Statistics. Knowledge and interest in industrial engineering and operations management would be an added plus to this position.

Candidates should send in their resume / CV along with PDF copies of their relevant publications and scanned copies of their degree transcripts, course grades and certificates (B.Eng, M.Eng and Ph.D.) by email (nagarajan@sutd.edu.sg) to Asst. Prof. Nagarajan Raghavan with a subject title – “Application for Post-Doc Position in Prognostics“.

Suitable candidates will be contacted for Skype Interview. Positions begin immediately and remain open until the right candidate is found. This post-doc position is available for a period of 12-18 months.

Ph.D. Positions Available

Job Description – Several Ph.D. positions are available in the following areas. If your knowledge and/or interest matches any of these, please send in your resume / CV along with PDF copies of relevant publications (if any) and scanned copies of your degree transcripts, course grades and certificates (B.Eng and/or M.Eng) by email (nagarajan@sutd.edu.sg) to Asst. Prof. Nagarajan Raghavan with a subject title – “Application for Ph.D. Position“. Do note that SUTD does not make it mandatory for applicants to have a Master degree for the PhD admissions. There are several scholarships available for PhD study at SUTD including the President’s Graduate Scholarship, SUTD PhD Scholarship, A*STAR SINGA Scholarship etc… The stipend (assured for 4 years) is tax-free and can range from S$2,000 per month to S$3,500 per month depending on the type of scholarship and immigration status of the applicant.

  • Prognostics and System Health Management (PHM).
  • Reliability Modeling.
  • Kinetic Monte Carlo (KMC) Simulations.
  • Physics of Failure Modeling.
  • Bayesian Statistics and Machine Learning Applied to Engineering Problems.