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increasingly complex world. Your research during the postdoc period will investigate how technological expertise is incorporated into top management in a changing world, characterized by high uncertainty
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developed and applied directly in realistic collider analyses. The postdoc will be hosted at TDB, co-supervised by both groups, and will work at the interface of scientific computing, machine learning
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applying existing tools and developing new ones, including machine and deep learning methods. There will be opportunities for development of new cell line and animal models and testing, evaluation, and
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field (industry postdoc counts) Hands-on experience training or post-training AI models Additional Preferences: Proficiency in Python and deep experience with ML/Deep Learning frameworks (e.g., PyTorch
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field (industry postdoc counts) Hands-on experience training or post-training AI models Additional Preferences: Proficiency in Python and deep experience with ML/Deep Learning frameworks (e.g., PyTorch
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 days ago
(e.g., bash scripting, python, Fortran, C++ and CUDA ), expertise in computational modeling (such as Molecular Dynamics, virtual screening, free energy calculations and Deep Learning), successful
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with Bayesian statistical modelling and deep learning applied to biological or food systems Documented experience with natural language processing of domain-specific text and transformer-based models
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/ College of Engineering / UC Berkeley Position overview Position title: Postdoc Researcher Salary range: The UC postdoc salary scales set the minimum pay determined by experience level at appointment
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Radboud University Medical Center (Radboudumc) | Nijmegen, Provincie Gelderland | Netherlands | about 1 month ago
experience with deep learning (e.g., PyTorch), ideally geometric/graph neural networks, equivariant models, or generative models. Interest in applying AI to molecular or biological problems; prior
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modalities, and development of scalable machine learning pipelines. Work will be entirely computational and based in Seattle, with no field deployment responsibilities. Responsibilities: The postdoc