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Field
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experience of using UK biobank data and human imaging would be desirable. The applicant should have proven programming experience including Python and R as well as using HPC and GPU environments
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reasoning or tool-augmented LLMs, RL (RLHF/RLAIF/online RL), or foundation models for science, Software engineering skills (Python) and experience with modern DL stacks (PyTorch) and multi-GPU training
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. Innovative visualization tools and highly automated analytical pipelines powered by GPU technology. Mentorship from experienced scientists in data analysis and management, with an expertise in delivering high
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GPU clusters and high-performance computing facilities to accelerate your research. Good transport links in a central location with a free job ticket as a contribution to climate protection Low-cost
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Optimal Control Theory Strong programming skills in C++/Python/MATLAB Familiarity with parallelization and high performance computing (CPU and GPU friendly code) Experience with Machine Learning, generative
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will have access to extensive computing resources, including access to ~100 Nvidia H200 GPUs. Applicants should hold a Ph.D. in computer science-related or biomedical research fields relevant
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, including GPU systems capable of running large-scale AI workflows. Applicants should submit online at https://www.princeton.edu/acad-positions/position/39681 and include a cover letter, curriculum vitae
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managing experiments using GPUs Ability to visualize experimental results and learning curves Effective inter-personal and team-building skills Self-motivated with an ability to work independently and in a
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) Experience in maintaining high-quality code on Github Experience in running and managing experiments using GPUs Ability to visualize experimental results and learning curves Effective inter-personal and team
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the university's research priorities and technological advancements. Performance Optimization: Analyze and optimize the performance of AI models and HPC applications focusing on GPU-enabled computing. Implement