Postdoctoral AI Researcher in AI/ML for Foundation Models, Scientific Applications, and AI Systems
- foundation model training, evaluation, scaling, and adaptation
- agentic workflows, tool-augmented models, and AI systems
- bespoke scientific applications of AI/ML, including the life sciences
- alternative architectures and systems-level approaches to modern AI
- Postdoctoral AI Researchers conduct research under the general supervision of one or more Kempner faculty members
- The appointment is for one year; reappointment may be possible for up to a total of three years, contingent on funding, project needs, satisfactory performance, and mutual interest.
- This is a full-time, benefits-eligible postdoctoral appointment based at the Kempner Institute at Harvard University.
- Due to the importance of in-person mentoring and collaboration, this position is based on campus, full-time, at Harvard University. Remote work for this position is not possible.
- PhD in computer science, statistics, electrical engineering, applied mathematics, computational biology, or a related quantitative field required by the expected start date.
- Candidates must have received their PhD on or after September 15, 2024, or be on track to complete all PhD requirements by the expected start date of October 15, 2026.
- Demonstrated expertise in modern AI/ML, including deep learning and hands-on experience with frameworks such as PyTorch or JAX.
- Strong publication record in leading venues such as ICML, ICLR, NeurIPS, or comparable conferences and journals.
- Demonstrated experience implementing, training, evaluating, or fine-tuning modern machine learning models.
- Strong programming skills in Python and experience building and maintaining research code.
- Demonstrated ability to use modern AI-assisted and agentic coding tools effectively, such as Claude Code, Codex, or similar systems, in research and development workflows.
- Ability to work effectively in a collaborative research environment and communicate technical work clearly.
- Experience with foundation model training, post-training, adaptation, or evaluation
- Experience with agentic workflows, tool use, retrieval systems, or related AI systems
- Experience with large-scale datasets, distributed training, or high-performance computing environments
- Interest in scientific applications of AI/ML, including the life sciences
- Interest in alternative architectures and systems-level approaches to AI
- CV
- A research statement of no more than 2 pages describing your experience in foundation models, agentic systems, scientific applications of AI/ML, and/or systems-level approaches to modern AI. Please be specific about your individual contributions.
- References - 2-3 required
- Please give the emails of up to 2 individuals who can describe your previous related work.
- Referees will be contacted to submit the letters directly to the Kempner Institute.
- The application will not be considered complete until all letters have been received.
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