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. Requirements: PhD completed less than 7 years ago in Computer Science or related areas; experience in machine learning and data science (supervised/unsupervised models, recommendation and evaluation/robustness
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remodeling and conformational engineering De novo and semi-rational enzyme design Directed evolution theory and workflow development Library design strategies (focused, combinatorial, and machine-learning
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chemistry, materials chemistry, geochemistry, drug discovery, artificial intelligence and machine learning, and emerging directions in quantum computing. This position functions as a senior research leader
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seeking a postdoctoral candidate in the area of federated learning and wireless communications. The candidate must hold (or about to complete) a PhD in the related fields. The candidate will be involved in
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-performance computing resources suitable for large-scale machine-learning and foundation-model experiments. Your role We are seeking a highly motivated Postdoctoral Researcher to join the FNR AI-HPC 2025
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dramatic upheaval as a result of rapid technological change driven simultaneously by digitization, the application of artificial intelligence and machine learning to all facets of company, economic, and
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of Artificial intelligence for De-Novo molecular design Machine learning/Neuronal networks to develop novel drug discovery tools Molecular modeling and simulation Theoretical biophysical medicinal chemistry Deep
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structures, and time-dependent processes spanning molecular and cellular scales. We encourage the use of theory and computation as well as experiment, and welcome applicants who use machine-learning and
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of deep learning in many disciplines, particularly computer vision and image processing. Consequently, coding architectures based on deep learning and end-to-end optimization have been proposed [Ding 2021
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psychoactive substances, in seized drug products or clinical samples. The candidate will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models