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LevelMaster Degree or equivalent Skills/Qualifications Solid background in Machine Learning and Deep Learning. Experience or interest in agentic AI frameworks (e.g., LangChain, LangGraph, AutoGen, or similar
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Assistant/Associate Professor of Practice in Human-AI Interaction, Machine Learning, and Data Design
developing, training, and deploying advanced AI, data, and machine learning systems from a human-centered innovation lens. Preference will be given to candidates whose background includes i) experience
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of relevant research experience. Background conducting quantitative research in healthcare. Technical Skills or Knowledge: Proficiency in optimization, statistics, machine learning, econometrics, or AI
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machine learning-driven digital twins for predictive combustion modeling. The research program will cover a wide range of e-fuels (H₂, NH₃, CH₃OH, DME, OME) and their applications in industrial furnaces
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AI and data science, particularly in dynamic settings where observations are collected sequentially and decisions influence future outcomes. This project will develop novel machine learning and
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, calorimetry, and synchrotron experimental measurement techniques. Knowledge of AI-based and machine-learning methods is also beneficial. For further information about a specific subject see General syllabus
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statistical and machine learning methods applied to large claims and electronic health record databases and multimodal data, including physiological waveforms and medical imaging. We foster a collaborative and
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, engineering computation, operational research, management science and applied statistics, FinTech, data science and machine learning. There are currently 56 academic staff and about 154 research personnel in
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related to modeling and simulation of biological systems, 3) very good IT skills, in particular the ability to program in Python, 4) very good knowledge of machine learning methods, neural networks, and
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of engagement. Read more at: Department of Computing Science Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven