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Field
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Strong foundation in CFD, Programming proficiency such as Python, AI/ML techniques, Experience with parallel computing on CPU/GPU cluster, use of CUDA, MPI is a plus. Experience Experience with open-source
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | 3 months ago
. The researcher(s) will be provided access to state-of-the-art supercomputing facilities with advanced GPU and data storage capabilities. Additionally, opportunities will be available for collaborations. Duties
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mathematics and engineering. The Interpretable Machine Learning Lab has dedicated access to high-performance CPU and GPU computing resources provided by Duke University’s Research Computing unit and state
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and H100 GPUs, combined with pre-processed large-scale biobank data such as UK Biobank and ADSP, enabling you to work at the scale required for breakthrough research. The role offers exceptional
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library research and field explorations. Run experiments, analyze results, and prepare research outputs. Execute large-scale training jobs on GPU clusters, track metrics, visualize findings, and contribute
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programmer, some hands-on experience in programming is mandatory for all 3 positions. Experience with compiled languages (C/C++/Fortran) and/or GPU programming is a strong advantage for positions 2–3. Note
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completed Ph.D. within the last 5 years in Computational Biology, Bioinformatics, Machine Learning, Artificial Intelligence, Virology, or a related field Strong programming skills in Python, R, or Julia, with
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will create a personalized training and development plan with the supervisor. Minimum Qualifications Currently has or is in the process of completing a PhD, MD/PhD, DPhil or equivalent terminal degree
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high performance computing cluster Proficient programming experience in Python and libraries (e.g., Pytorch, TensorFlow) Experience in maintaining high-quality code on Github Experience in running and
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Mathematica and Python with an interest in GPU programming. These required and desired skills should be demonstrated by presenting an existing body of code and/or peer-reviewed publications. Additional