Research Fellow (Machine Learning)

Join Our Team at the School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore

The School of Physical and Mathematical Sciences (SPMS) at NTU Singapore hosts research and education activities in two divisions: Division of Mathematical Sciences (MAS) and Division of Physics and Applied Physics (PAP). MAS covers diverse topics ranging from pure mathematics to the applications of mathematics in cryptography, computing, business, and finance. PAP covers many areas of fundamental and applied physics, including quantum information, condensed matter physics, biophysics, and photonics. Over the years, SPMS has attracted talented individuals from around the world and Singapore to join as scientific leaders and researchers.

Evaluating the errors associated with Machine Learning (ML) predictions is an active area of research crucial for the safe and effective deployment of ML techniques. These errors have two components. The epistemic error refers to the degree of similarity between the analysed data and the ones the uses to train the machine. The uncertainty error refers to the noise in the training and test data.

The Pica Ciamarra groups is looking for a highly motivated and qualified Research Fellow with the ambition of producing high quality research to develop physics-inspired model for error estimation in ML. The Research Fellow will be involved in research design, data analysis, model development, numerical simulations, and report/paper writing, in strict collaborations of partners of the CNRS@Descartes.

The Research Fellow is expected to perform state of the art numerical simulations using computing resources from National Supercomputing Centre (NSCC) Singapore. The incumbent has freedom to design the nature and type of deep learning simulations.

Key Responsibilities:

• Devise physics-inspired ML approaches to estimate epistemic and uncertainty errors.

• Construct synthetic dataset suitable to the designed models.

• Implement models for error estimation in classification and regression tasks.

• Interact with partners of the CNRS@Descartes project to foster the application of the developed models to use cases.

• Validate models via large scale simulations on National Supercomputing Centre (NSCC) Singapore.

Job Requirements:

• Ph.D in physics or related disciplines

• 2 years of relevant experience is preferred

• Experience in Machine Learning and modeling

• Knowledge of Python and C++

• Able to multitask and work in a multidisciplinary team, demonstrate initiative and work independently with strong interpersonal skills.

• Good publication record in international journals

The College of Science seeks a diverse and inclusive workforce and is committed to equality of opportunity. We welcome applications from all and recruit on the basis of merit, regardless of age, race, gender, religion, marital status and family responsibilities, or disability.

We regret to inform that only shortlisted candidates will be notified.