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Professor level. The ideal candidate will be at the forefront of research that integrates modern machine learning methods with economic theory and econometric analysis. We are particularly interested in
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your machine learning expertise to cutting-edge automated optimisation challenges? We are seeking a Research Fellow in Digital Chemistry and Engineering to combine self-optimising flow reactor technology
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experience working with frontend/backend web development, machine learning, or high performance computing would be desirable, as would any experience designing or delivering training courses. This post is
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order to detect microseismic events, characterise their sources, and image geophysical perturbations caused by industrial activities. The development of machine learning methods to efficiently process
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inference, and Machine Learning methods. In addition to leading their own research projects, the appointed candidate will have the opportunity to contribute to the projects of PhD students in the group, as
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Subject area: Drug Discovery, Laboratory Automation, Machine Learning Overview: This 36-month PhD studentship will contribute to cutting-edge advancements in automated drug discovery through
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) Neuromodulation approaches (TMS, tDCS, TUS) Neurogenetics Computational modelling (machine learning, reinforcement learning) Our research bridges scales (local circuits to global networks) and species (humans, mice
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proficiency in Python (e.g., NumPy, Pandas, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with supervised
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programmes. We are especially keen to hear from applicants with expertise in areas such as Machine Learning and Deep Learning, Generative AI, Data-Centric and Responsible AI, or AI for real-world applications
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expertise across autonomy, machine learning-based control, multi-agent systems and Decision-making. Excellent communication skills (both written and oral) with ability to communicate complex and conceptual