1,115 machine-learning-"https:"-"https:"-"https:"-"Fraunhofer-Gesellschaft" Fellowship positions
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machine learning and AI techniques to improve prediction of contaminant transport, sediment dynamics, and ecosystem exposure in complex fjord environments. The research will benefit from extensive
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successful candidate, you will: This project with Dr. Nagarajan Vaidehi involves developing and application of interpretable machine learning methods to uncover allosteric regulation of disordered regions in
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analysis, PDE-constrained optimization and optimal control, numerical analysis and scientific computing risk-averse and fractional models, digital twins and data-assimilating models, machine learning and
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Qualifications Experience with machine learning and AI models. Ability to communicate clearly with colleagues, Data presentations Scientific publication experience Special Instructions Priority Application Review
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Responsibilities: Conduct research in Systems Biology to study cell signaling Perform large-scale signaling analysis or protein-interactome analysis Utilize machine learning-based biological data mining and analysis
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position as Postdoctoral Fellow in law at the UiS School of Business and Law, Department of Accounting and Law. The Postdoctoral Fellow will be affiliated with the research project AUTO-MARE – Autonomous
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machine learning, spatial audio and audio-visual AI into groundbreaking creative technology. About you We seek a talented Research Fellow to investigate generative audio AI technology for production
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of power and propulsion systems, heat and mass transfer, thermo-fluid systems simulation and programming, as well as exposure to software platforms and algorithms for Artificial Intelligence, Machine
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research projects in computer vision, machine learning, AI, and robotics. Projects may include physically-grounded AI guidance agents, modeling of multimodal data, and generative AI systems for situated
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associated risks to U.S. farmlands and agricultural resources. Learning Objectives: During this appointment, the participant will develop hands-on experience working within an interdisciplinary research team