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: Ability to work with large structured and unstructured datasets, and GPU-accelerated computing. Proven experience with Large Language Models. Required Skill/Ability 3: Sound background in theoretical and
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-College of Engineering Department: UNT-Computer Science & Engineering-130310 Job Location: Denton Salary: Competitive salary based on experience FTE: 1.00 Retirement Eligibility: ORP Eligible About Us
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physics, mathematics or any related field; correspondingly, Postdocs hold a PhD or equivalent degree in the abovementioned fields. What we offer State of the art on-site high performance/GPU compute
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, Matlab Preferred Qualifications: Experience in thermos-fluids in porous media. Experience in High-Performance Computing (HPC) on CPU or GPU platforms. Experience in mentoring of graduate and undergraduate
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experience with coding in C++, python/matlab. GPU programming is a definite plus. You have experience in applying deep learning to solve computational imaging problems. Experience with inverse problems such as
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FWO-UGent funded bioinformatics postdocs: Unveiling the significance of gene loss in plant evolution
environment Access to state-of-the-art tools and computational infrastructure, including CPU/GPU clusters Opportunity to contribute to cutting-edge research in plant evolution and genomics Support
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developing machine learning surrogates and emulators for dynamical systems. Proficiency in managing large datasets and training with GPU-enabled computing resources. Expertise in numerical optimization and
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of urbanization on precipitation, and aerosol-cloud interactions Strong modeling skills and high-performance computing experience Experience with model code development, and strong programming skills (e.g., Fortran
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The X-ray Imaging Group (IMG) of the Advanced Photon Source (APS) is seeking a postdoctoral researcher with expertise in computational science and image processing to develop innovative methods
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environments, cloud computing, or GPU-accelerated machine learning Background in Monte Carlo Tree Search (MCTS) or reinforcement learning for sequence generation Familiarity with biological sequence alignment