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hemocompatible coating strategies to improve membrane–blood interactions. - Model and optimize membrane performance using computational tools, machine learning, and artificial intelligence Work Plan - Synthesis
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the analysis of complex biomedical data using state-of-the-art AI and agentic system approaches, as well as the development of novel machine learning and deep learning algorithms. Your work will range from
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disciplines. Familiarity with contemporary AI systems (e.g., machine learning, generative models) at a conceptual or applied level. Experience with qualitative or mixed research methods (e.g., ethnography
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skilled in object-oriented coding (preferably Python) and data analysis; affinity with machine learning and explainable AI techniques, preferably in a geoscience context; good social skills. As a university
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of their effective start date. This position is for a post-PhD trainee preparing for a research scientist career path. The planned position will provide a transition to career independence through
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*• Experience in Python or another programming language (projects, GitHub repositories, courses, scientific use).• Training or experience in machine learning and data science applied to environmental or energy
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
join forces to push the boundaries of data‑driven discovery. Our mission is two‑fold: to advance fundamental theory in probability and machine learning, and to translate those breakthroughs into high
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: research experience in skin biology, tissue repair, reparative medicine, epigenetics, or RNA biology experience in multi-omics integration, advanced statistics, machine learning, or biological data
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humanities disciplines. Familiarity with contemporary AI systems (e.g., machine learning, generative models) at a conceptual or applied level. Experience with qualitative or mixed research methods (e.g
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fully funded PhD position in the area of safe data-driven system identification for cyber-physical systems, offered by our research group at the intersection of control theory, machine learning, and