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The Faculty of Engineering at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) invites applications for an Assistant Professor of Machine Learning in Digital Health (salary group W1
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Postdoc in Generative Machine Learning for Biomedical Data | Human Technopole, Milan Build the science that shapes the future of human health. Application closing date: 21.02.2026 Join a place where
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microscopy data analysis, chemometrics, and machine learning. This position is ideal for a researcher who enjoys working at the interface of imaging, data science, and environmental monitoring. The project
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on healthcare, research, and teaching, we bear a unique responsibility – ideally with you on board! The position is based at the Institute of Medical Informatics within the research group Machine Learning
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-Holstein (UKSH), is seeking a PhD student with a strong background in statistics, machine learning (ML)/artificial intelligence (AI), or bioinformatics. Our group develops novel computational and statistical
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Postdoctoral Researcher Position in Ecological Knowledge-Guided Machine Learning at Aarhus Univer...
quality modelling, with focus on Knowledge-Guided Machine Learning. The position is a rewarding opportunity to be integrated in an excellent freshwater group. The department’s research and advisory
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Teaching Faculty Position - Machine Learning - Generative AI: Machines that Create Mohamed bin Zayed University of Artificial Intelligence: Academic Appointments: Undergraduate Division Location Abu
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2,900 work in administration and organisation. We are looking for a/an University assistant predoctoral/PhD Candidate Optical Quantum Computing and Machine Learning 51 Faculty of Physics Startdate
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machine learning methods and applications focusing on the aspects outlined above, inspired by the needs of societally relevant applications, e.g. from neuro- or plant sciences. Advance our understanding
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SD- 26053 PHD IN ULTRA-FAST MACHINE-LEARNING INTERATOMIC POTENTIALS FOR NANOINDENTATION OF TIC MA...
PhD candidate to develop and apply ultra-fast machine-learning interatomic potentials (UFPs, Xie et al., npj Comput. Mater., 2023, 10.1038/s41524-023-01092-7 ) for long, multi-million-atom molecular