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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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and Machine Learning Researcher will report to Ayan Paul, Research Scientist at EAI. There will also be opportunities to work on industry collaborations. Responsibilities will include building an ETL
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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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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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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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positions in the area of Artificial Intelligence (AI) and Machine Learning (ML) in Drug Discovery. This is a unique cluster hire initiative spanning the College of Pharmacy, Life Sciences Institute (LSI), and
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of optimization and machine learning. • Knowledge of reinforcement learning and black box optimization would be a plus. Skills • The candidate must be comfortable with algorithmic development using
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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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Applications are invited for a Postdoctoral Research Associate in Machine Learning for Chemistry to work in the research group of Professor Volker Deringer at the Department of Chemistry. About the