Job Description

We are looking for a highly motivated and skilled Research Assistant to join a project that integrates classical machine learning and AI with quantum computing on graphs datasets, with applications in drug development. The selected candidate will work on a research project of Duke-NUS AI + Medical Sciences Initiative (DAISI), which serves as the School’s hub for Academic AI and Data Science. This project will be conducted in collaboration with the Centre for Biomedical Data Science (CBDS) at Duke-NUS Medical School and the Centre for Quantum Technologies at National University of Singapore (NUS).

The selected candidate will perform a range of research activities focused on developing new AI algorithms and hybrid classic-quantum approaches for drug discovery under the supervision of the Principal Investigator or his/her designate. The candidate’s responsibilities will include, but are not limited to the following: 

• Perform tasks and support all aspects of the research project.
• Carry out algorithmic development and data analysis.
• Implement state-of-the-art algorithmic improvements from recent literature in graph and geometric deep learning.
• Develop new geometric deep learning algorithms, from theory to experimental implementations, or adapting existing ones to problems in drug discovery.
• Perform other related duties incidental to the work described therein.

Job Requirements

• Bachelor’s / Master’s degree in a related scientific area (e.g., Physics, Mathematics, Computer Science) with demonstrated hands-on experience in developing deep learning, geometric deep learning, and generative AI algorithms. Candidates with higher credentials may be considered for Research Associate appointment.
• Experience with PyTorch, PyTorch Lightning, or equivalent deep learning frameworks.
• Proficiency in Python coding, with exposure to bash and multiprocessing environments. 
• Familiarity with high-performance computing (HPC) environments and GPU programming.
• Strong teamwork skills with the ability to manage multiple priorities and thrive in a diverse work environment.

We regret that only shortlisted candidates will be notified.