Research Fellow (Cancer Science Institute, Prof Yang Zhang's Lab) 1 1

Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal.

NUS Career Portal link: https://careers.nus.edu.sg/job/Research-Fellow-%28Cancer-Science-Institute%2C-Prof-Yang-Zhang's-Lab%29-1-1/34271-en_GB/

We regret that only shortlisted candidates will be notified.

Job Description

To conduct high-impact research in AI-driven computational biology, with emphasis on template-guided TCR design, pMHC-TCR complex modeling, and single-cell foundation models for cancer and immunology applications. The position supports the NUS AI4SCI and Computational Biology team by advancing method development, benchmarking, model implementation, and biological validation of AI models for molecular and cellular systems.

Duties & Responsibilities

• Develop and benchmark AI/ML methods for TCR design, including template retrieval, CDR3 optimization, off-target-aware selection, and structure-based filtering/refinement.
• Develop single-cell foundation model components for robust zero-shot/few-shot cell embeddings, perturbation prediction, and interpretation of cancer and immune-cell states.
• Implement, train, evaluate, and document computational models using large-scale protein, TCR, pMHC, and single-cell datasets.
• Analyze model outputs, prepare figures and manuscripts, and contribute to grant reports, presentations, and collaborative research discussions.
• Work with lab members and collaborators to translate model development into biologically meaningful applications in cancer immunology and computational biology.

Qualifications

• PhD in computational biology, bioinformatics, computer science, machine learning, biophysics, or a closely related discipline.
• Strong experience in AI/ML model development and scientific programming, preferably in deep learning, representation learning, generative modeling, protein/TCR modeling, or single-cell analysis.
• Demonstrated ability to conduct independent research, analyze complex biological datasets, and communicate results clearly in written and oral form.
• Experience with Python/PyTorch and large-scale computational workflows is highly desirable.
• Ability to work collaboratively in an interdisciplinary research environment.