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their thesis under our supervision. This project builds upon our prior work in Multi Agent Reinforcement Learning (MARL) for real-world use-case of aerial ad hoc networks (see our paper ) and aims to advance
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classification systems (RISE, GradCAM, EigenCAM, Integrated Gradients, ViT Shapley, etc.) Implementation and execution of experiments in the field of machine learning (ML): Cleaning, preparing, splitting
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Artificial Intelligence (AI) and Machine Learning (ML). In this position students will contribute to research projects in CKL and as part of their education, will also engage in a dedicated 6-months internship
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acceleration of large-scale machine learning workloads Perform characterization and modeling of electronic and optical devices Develop hardware-aware machine learning models incorporating electronic and optical
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want to hear from you! Your Job: Work on a wide range of computer vision and machine learning methods and applications focusing on the aspects outlined above, inspired by the needs of societally relevant
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Max Planck Institute for Intelligent Systems, Tübingen, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | 8 days ago
realistic human and animal avatars for use in research, film, virtual reality, biology and medicine. Using unique 3D & 4D capture facilities, machine learning, computer vision and advanced graphics, we
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Do students receive financial aid? All doctoral positions are fully funded, including social benefits. Students also receive funding to attend conferences and other events related to their research, and have access to outstanding facilities. Do I need to know English? Yes, English is the...
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heavily relies on empirical determination of key model parameters. By combining protein structure descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange
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for part-time employment. Starting date: 14.01.2026 Job description:PhD position on physics-based machine learning modeling for materials and process design Reference code: 2026/WD 1 Commencement date
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. The position involves close collaboration with experts in cardiovascular simulation and Scientific Machine Learning. Your tasks: Development and comparison of data driven models for the prediction of stresses in