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Field
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, Mathematics, Physics, or a closely related field. Proficiency in machine learning libraries (e.g, scikit-learn, PyTorch, and transformers) and data analysis tools (e.g., pandas, NumPy, and CuPy). Hands
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Trustworthy AI Systems NSF Research Traineeship , the Digital Trade and Data Governance Hub , the Center for Machine Learning, and the Human-Computer Interaction Lab at UMD, and the Center for Equitable AI and
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Trustworthy AI Systems NSF Research Traineeship , the Digital Trade and Data Governance Hub , the Center for Machine Learning, and the Human-Computer Interaction Lab at UMD, and the Center for Equitable AI and
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Machine Learning. The research objective of this position is to design and conduct studies on human perception, to investigate the effect of different visualization techniques on human users. A particular
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care, our work is aimed at creating spaces for collaboration, critical analysis, reflection, and learning. About the organisation At the Faculty of Behavioural, Management and Social Sciences (BMS), we
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to join our team. Our lab focuses on developing and applying innovative statistical machine learning methods, single-cell multi-omics, and systems immunology approaches to investigate immune-mediated
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, Mathematics, Physics, or a closely related field. Proficiency in machine learning libraries (e.g, scikit-learn, PyTorch, and transformers) and data analysis tools (e.g., pandas, NumPy, and CuPy). Hands
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machine learning. The successful applicant will participate in research involving human computation, knowledge discovery, machine learning, and data science. The position will provide the opportunity
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comes with no teaching obligations BASIC RESPONSIBILITIES AND OBLIGATIONS Conducting research related to the scientific project titled “Calculus of variations in machine learning problems”, in particular
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one or more of the following areas is a BIG PLUS: data science (machine learning and AI), cancer biology, animal physiology, organic chemistry, E3-ubiquitin biology, and gene editing. In all cases