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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 21 hours ago
: * Introductory proficiency in Python, R, or another programming language * Prior exposure to machine learning or AI techniques in clinical research * Experience using deep learning frameworks * Familiarity with
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at the interface between computational physics, machine learning and neuromorphic computing? Do you thrive for both fundamental and societal impact of your research? If so, we have a project for you
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skills, proficiency in quantitative analysis of large datasets and working with pre-trained machine learning models is desirable but not essential To apply online for this vacancy and to view further
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Department: Department of Machine Learning and Data Processing – Faculty of Informatics Deadline: 14 Jul 2025 Start date: preffered September 2025 / by mutual agreement Job type: full-time Job
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position from 01.10.2025 of a Postdoc Position at the Research Group Data Mining and Machine Learning at the Faculty of Computer Science, University of Vienna under the supervision of Prof. Claudia Plant
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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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for computational biology and a track record of excellence in graph machine learning and multi-omics data integration? Look no further – an exciting Postdoc opportunity awaits you at the Novo Nordisk
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About us The Department of Informatics is seeking to appoint a postdoctoral research fellow with an excellent track record in knowledge graphs, semantic technologies, and machine learning. Topics
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. Responsible and dependable with attention to deadlines and skills in time management. Motivated, creative, and ready to learn new things. Skill in handling multiple competing priorities. Specialized skills in
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contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will be working primarily with