Research Fellow
2 weeks ago
Join Our Team at the School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore
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 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.-
- 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
Hiring Institution: NTU
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