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a machine learning model (foundational model) to propose protocols of sequential induction of transcription factors to generate desired cell subtypes. The project will be conducted in close
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at international conferences. You hold a PhD in computational biology/chemistry, machine learning or a related quantitative field. You have a solid publication record and demonstrated experience with advanced
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Lab at Princeton University aims to recruit a postdoctoral fellow or more senior research position to work on projects related to the development of AI/machine learning approaches for chemical and
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for embedded and GPU platforms. Collaborate with ARSPECTRA engineers and surgeons to create a complete AR guidance pipeline : tracking, SLAM, gaze, user interface Your profile PhD in machine learning
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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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and uncertainty mapping at satellite, airborne and drone levels. You will explore advanced retrieval techniques, including spatio-temporal regularization, and hybrid methods with machine learning and
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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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of Experimental Medicine and Therapy Research, in the laboratory of Dr. Melanie Werner-Klein (https://www.experimentelle-medizin.de/erforschung-der-metastasierung ), we are inviting applications for a highly
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theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers the opportunity to work with