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Field
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DeepMD, NEXMD, i-PI, MixPI, NWChem, CP2K, or comparable packages. Experience with GPU acceleration, distributed training, high-performance computing systems, or C/C++ scientific software development
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/ coarse- grained approaches) Experience with enhanced sampling techniques; computational biophysics/chemistry Usage of high-performance computing clusters, preferably GPU-based computing Proficiency in
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heat transfer processes Use of advanced numerical methods (CFD, LBM, hybrid models) Utilization of high-performance computing (HPC, GPU) Analysis and validation of numerical results Optimization
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, or population genetics Deep learning for sequence, EHR, or imaging data High-performance and GPU computing environments Excellent candidates from adjacent quantitative fields are encouraged to apply. The Research
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(UTC) Country Italy Type of Contract Other Job Status Other Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a
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University of California, Los Angeles | Los Angeles, California | United States | about 11 hours ago
Nextflow or Snakemake, version control such as Git, and reproducible computational environments is preferred. Familiarity with GPU-accelerated genomics, high-performance computing, or cloud-based analysis
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7 Sep 2026 Job Information Organisation/Company Fundació per a la Universitat Oberta de Catalunya Research Field Computer science Researcher Profile Recognised Researcher (R2) Positions Postdoc
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Standard Hours per Week 40 Full Time or Part Time? Full Time Shift Day Work Schedule Summary VP Area Academic Affairs Department 00062 - School of Computing Location Campus City Salt Lake City, UT Type
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8 Jul 2026 Job Information Organisation/Company Saarland University Department Data Driven Simulation and Analysis in Material Science Research Field Computer science Researcher Profile Established
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architecture of entirely new foundation models, directly advancing the frontier of computational biology and machine learning. You will also implement parallel systems capable of training such models across