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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 23 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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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
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foundation models, directly advancing the frontier of computational biology and machine learning. You will also implement parallel systems capable of training such models across large GPU clusters on cryoSTEM
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, validation, calibration, and inference. Working with large, longitudinal, structured and unstructured datasets in Linux and high-performance or GPU-accelerated computing environments. Applying rigorous methods
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). Practical experience with cloud computing platforms (e.g., AWS, GCP, Azure). Additional Qualifications Experience with multi-GPU model training and large-scale inference. Familiarity with modern AI