SUCCESSFUL CANDIDATES WILL BE RESPONSIBLE, BUT NOT LIMITED TO:

Conduct research and development of LLM tuning paradigms.
Directly contribute to experiments of privacy preserving LLM tuning, including designing experimental details, writing reusable code, running evaluations, and organizing results.
Directly contribute to Agentic AI of code generation, compilers, debugging, etc.
Evaluate utility, privacy, and efficiency trade-offs among compared baselines.
Publish high-impact research in top-tier venues. (e.g., NeurIPS, ICLR, ACL, IEEE S&P).
Contribute to open-source tools and frameworks; and potentially guide junior researchers or interns.

REQUIREMENTS:

Degree in Computer Science, Machine Learning, or related field.
Strong background in deep learning, natural language processing, or large-scale optimization.
Demonstrated experience in working with open-source LLMs. (e.g., fine-tuning, instruction tuning, prompt engineering)
Familiarity with privacy-preserving machine learning concepts. (e.g., federated learning, synthetic data).
Strong programming skills in Python and experience with ML frameworks. (e.g., PyTorch, HuggingFace Transformers).
Good written and verbal communication skills.
Please submit your CV, a short research statement (if available) to Yinhaiyan@cfar.a-star.edu.sg and LiJing@cfar.a-star.edu.sg.
The above eligibility criteria are not exhaustive. A*STAR may include additional selection criteria based on its prevailing recruitment policies. These policies may be amended from time to time without notice. We regret that only shortlisted candidates will be notified.
Type of Employment : Full-Time
Minimum Experience : 1 Year
Work Location : Fusionopoli