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. Empa is a research institution of the ETH Domain. The group Multiomics for Healthcare materials at Empa, St. Gallen generates and integrates multi-modal biomedical datasets with the aim to inform
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teaching assistantship. The ideal candidate will have a strong foundation in Python programming and hands-on experience with deep learning frameworks such as TensorFlow or PyTorch. Applicants with a
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areas, could include deep learning (e.g. Long Short Term Memory - LSTM), statistical baselines (e.g. Autoregressive Integrated Moving Average - ARIMA, Kalman filters) and transformers (e.g., spatio
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of short-axis MR image sequences. Training You will be based at the Vision Computing Lab within the School of Computing Sciences, which specializes in deep learning for medical image analysis and neural
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biosynthetic gene clusters and other molecular features relevant to natural product drug discovery Validating machine-learning and deep-learning models to predict the chemical structures and bioactivity
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assessment criteria: Knowledge in energy technology, large language models (LLMs), deep learning, and Python programming. Meritorious qualifications include knowledge in power engineering, power electronics
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thermodynamics, with an emphasis on both theoretical and practical applications. Experience in machine learning and AI, particularly deep learning frameworks such as TensorFlow, and their application in fluid
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on the following assessment criteria: Knowledge in energy technology, large language models (LLMs), deep learning, and Python programming. Meritorious qualifications include knowledge in power engineering, power
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environment representations, deep reinforcement learning, and intelligent robotic behavior. Solutions will be validated in simulation (e.g., ROS/Gazebo, Isaac Sim) and real robotic platforms. The candidate is
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of Science and Freie Universität Berlin, Humboldt-Universität zu Berlin, and Technische Universität Berlin. The IMPRS-KIR traces the deep entanglements of knowledge and its resources from a long-term and