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computational methods using network-based analysis, machine learning and dynamic modeling. We are a young, dynamic team at the idyllic Dahlem campus and teach mainly in the Computer Science, Bioinformatics and
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foundational models, CNNs, and transformer models. Solid understanding of Python, app development paradigms and deep learning frameworks. Experience with Reinforcement Learning is a plus. Educational
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and data analysis skills (e.g., Python, R) Solid understanding of machine learning, deep learning, and data modeling techniques. Preferred Qualifications: Prior working experience with EHR data, machine
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deployment of AI and machine learning models that improve agricultural water decision-making. The position focuses on combining innovative AI/ML techniques with established physical-based and agent-based
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testing on 3D-printed mechanical parts. Develop a machine learning model for high-throughput evaluation of the mechanical properties of 3D-printed metallic parts. Collaborate with other group members
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management. Background in computational modeling and machine learning. Background Investigation Statement: Prior to hiring, the final candidate(s) must successfully pass a pre-employment background
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Machining and Integration Specialist - Job Number: 251658 Organization : Aerospace & Mechanical Engin. Job Location : Oklahoma-Norman-Norman Campus Schedule : Full-time Work Schedule: Monday
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-informed machine learning) and integrating uncertainty quantification into these workflows. You are familiar with environmental or soil science applications (e.g., carbon, nitrogen, biomass modelling). You
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) information-theoretic active learning, and c) capturing uncertainty in deep learning models (including large language models). The successful postholder will hold or be close to the completion of a PhD/DPhil in
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on machine learning, and developing and applying simulation methods and models for equilibrium and nonequilibrium molecular dynamics simulations. You will model meta-lactamases enzymes involved in resistance