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adapting large-scale codes developed for parallel computing. Record of excellence in professional achievement, as evidenced by a strong publication record. Demonstrated ability to carry out independent
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 22 hours ago
-lab.med.unc.edu/) has an open position for a computational postdoc to study the genetics of human disease. The successful candidate will analyze response QTLs (expression QTLs, splicing QTLs, chromatin
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computational approaches, including in vivo Massively Parallel Reporter Assays (MPRAs), to define the sequence basis and functional consequences of enhancer activity and to expand MPRA-based approaches to other
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Engineering Position Description The Materials Intelligence Research group of Prof. Boris Kozinsky at Harvard University is seeking researchers at the postdoc level to develop and apply first principles and
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functional genomics, human genetics, and in vivo experimental systems to understand enhancer function across regulatory and phenotypic scales. We develop and apply both experimental and computational
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Biology Stanford Cancer Center Postdoc Appointment Term: Open-ended. Appointment Start Date: ASAP Group or Departmental Website: https://rogala.stanford.edu/ (link is external) How to Submit Application
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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
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research resource for the department, ensuring reliable study execution and operational support for faculty research. In parallel, the postdoc will advance their own research program and collaborate with
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conferences (e.g., NeurIPS, SC, AAAI, or domain-specific venues like Fusion Science or Computational Materials). Collaborative mindset in team environments and across disciplines. Special Requirements: Postdocs
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computational experts to conduct efficient analyses leading to discovery in mammalian neuron genome structure-function data. In parallel, trains to achieve mastery and excellence in running code written by other