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. More information about the research group can be found here: https://www.kcl.ac.uk/research/shankar-hari-group-1. The groups’ programmatic work focuses on understanding how immune dysregulation drives
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. The position requires experience with at least one of the following: Data Science, Machine Learning, Computational Social Science, Big Data. Relevant skills could include statistical analysis, data management
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Computer Science, Electrical/Electronics Engineering, Data Science, Cyber-Physical Systems, or a closely related discipline. Machine Learning & Data Processing: You have solid experience developing and applying
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next-generation machine learning (ML) models that are both data-efficient and transferable, enabling more reliable catastrophic risk prediction, defined as the probability of exceeding critical safety
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experienced candidates may be considered for higher ranks. The primary role of the Assistant Professor of Practice is to create an engaging and meaningful learning experience for students in statistics and data
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to sustain an active research and publication agenda and to teach in the departmental undergraduate and graduate programs. Candidates with expertise in machine learning, big data, mathematical finance and
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application! We are looking for a PhD student for sustainable and resource-efficient machine learning. Your work assignments Machine learning has recently advanced through scaling model sizes, training budgets
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machine learning, computer vision, and medical image analysis, with publications in top-tier AI and medical image analysis conferences and journals, including CVPR, ICCV, ECCV, NeurIPS, MICCAI, TPAMI, TIP
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comprehensive platform for data extraction, analysis, and version control, providing access to highly curated datasets in a machine learning-friendly format. This PhD is part of the CARES project (Chemically
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landscape constrains or enables discovery. The project draws on tools from topological data analysis (e.g., persistent homology, Euler characteristic curves, discrete curvature), machine learning (e.g