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of applying molecular models at process scales, the project combines efficient mathematical concepts like automatic differentiation with backpropagation – the same concept that powers machine learning and
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at the interface of machine learning, statistics, and live-cell biology. The position is co-supervised by Prof. Olivier Pertz (Cell Biology) and Prof. David Ginsbourger (Statistics), and the student will be equally
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of information transmission, derive the theoretical models governing them and demonstrate their intriguing properties experimentally. The practical realizations of these topological metamaterials will be based
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experience with CAD/FEM software Experience in one or more of the following fields is a plus: simulation frameworks (e.g. SOFA, NVIDIA IsaacLab), ROS1/2, machine learning, computer vision Excellent
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recognized research team with leading experts in remote sensing, atmospheric modelling, emission quantification, and machine-learning. Integration into Empa's Atmospheric Modelling / Remote Sensing group with
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57,000/year Collaboration with an internationally recognized research team with leading experts in remote sensing, atmospheric modelling, emission quantification, and machine-learning. Integration
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, indicators and survey information. The division combines structural macroeconometric modelling with data-science methods for nowcasting, high-frequency indicators construction, machine learning, time-series
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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description PhD Position in Energy-Efficient Machine Learning for Wearable and Augmented Reality
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reinforcement learning for large language models (LLMs). Research directions include developing next-generation post-training algorithms, exploring diffusion-based approaches to reasoning with language models
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Simulation Framework The Computational Biology (CoBi) group, led by Prof. Dagmar Iber, develops data-driven, mechanistic models of biological systems using advanced imaging and computational tools. Our group