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how a tumour responds to therapy across biological scales, are both betting that structure beats brute scale. In this postdoc you will lead research on physics-informed (and physics-grounded) world
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information sciences. In parallel with basic research, we develop ideas and technologies further into innovations and services. We are experts in systems science; we develop integrated solutions from care
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modeling. This position offers a vibrant research environment with access to world-class high-performance computing clusters and opportunities to collaborate broadly across the departments of Physics
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methods are developed in parallel, the postdoc will develop systems and services that make biological data accessible to AI and computational tools, collaborating closely with the Human Protein Atlas (HPA
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knowledge in parallel/GPU computing. Job Duties Job Duty Doing research problems in the area of mathematical foundations of data science and machine learning. The postdoc will assist with ongoing research
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Posted on Tue, 04/07/2026 - 20:37 Important Info Faculty Sponsor First name: Stephen Faculty Sponsor Last Name: Hinshaw Stanford Departments and Centers: Molecular and Cellular Physiology Postdoc
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applications with related expertise. Experience with sequencing approaches to study RNA turnover or with massively parallel reporter assays(MPRAs) will be beneficial. Must Have Bioinformatics experience in
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, including parallel computing frameworks, C/C++, Julia and/or python Experience with training AI surrogate and/or inverse models Experience working in underground laboratories and/or clean rooms Excellent
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of human biological fluids (such as blood and urine), to ensure successful implementation in various clinical and exposomics applications. In parallel, methods for targeted metabolomics/lipidomics need to be
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develop computational fluid dynamic (CFD) tools that make exascale computing accessible to a broader set of users. The successful candidate will develop a massively parallel solver, capable of running