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position in machine learning (ML). This position offers a unique opportunity to conduct both basic and applied research in concert with collaborators working on diverse scientific and security problems
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The aim is to develop machine-learning models that describe how
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application! Your work assignments Spatio-temporal processes are everywhere in science and engineering, with applications ranging from weather prediction to cardiovascular medicine. Developing machine learning
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About the role This project will develop new machine learning techniques to enable efficient production of bespoke products, such as medicines in a high-volume, high-variation manufacturing setting
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) to develop accelerated AI, machine learning, and robotics algorithms with a strong focus on computational efficiency, memory reduction, and energy-aware deployment. The role targets foundation models
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that learn nonlinear cross-fidelity correlations. More broadly, scientific machine learning methods such as physics-informed neural networks (PINNs) and operator learning (DeepONet, Fourier Neural Operator
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Doctoral Researchers (PhD students) to work on deep learning methodologies for machine and robot perception. These positions are funded by the Horizon Europe project OPERA (Open Perception, Learning, and
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a machine learning model (foundational model) to propose protocols of sequential induction of transcription factors to generate desired cell subtypes. The project will be conducted in close
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based on machine learning tools for energy problems related to prediction. The application domains include both industry and climate changes. The first two months will be devoted to the study of
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relevant field (Informatics, Computer Science, Ecology, or Biology). strong Python programming skills proven experience with Machine Learning and image processing (Deep Learning/Object Detection) strong