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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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to contribute to the development of innovative machine learning solutions using deep learning and multimodal foundation models. Working closely with leading researchers, you will design, develop, and implement
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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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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