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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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ICT Services & Applications. Your role This position sits at the interface of machine learning, uncertainty quantification and computational biomechanics. You will work within the Legato group and in
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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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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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opportunity to join the ERC-funded project “ALPS - AI-based Learning for Physical Simulation”. Expected start date and duration of employment These are 1–year positions from 1 May 2026 or as soon possible. Job
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Area of research: Scientific / postdoctoral posts Job description: Postdoc (f/m/x) in Deep Learning and Digital Pathology 102827 Full time 39 hrs./week Neuherberg near Munich Partial Home Office
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. The successful candidate will be employed at the Department of Computer Science of the University of Luxembourg and have access to high-performance computing resources suitable for large-scale machine-learning and
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Postdoctoral Positions for Computational Genomics, Cancer Genetics, and Translational Cancer Biology
mechanism-driven AI and agentic AI frameworks (iGenSig-AI, G2K) that integrate biological knowledge with cutting-edge machine learning to transform omics data into actionable therapeutic insights
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The Section of Bioinformatics, DTU Health Tech is world leading within Immunoinformatics and Machine-Learning. Currently, we, together with a leading external pharma company party, are seeking a
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to test and compare strategies safely, calibrate models with real data, and support scenario-based decision-making. • Building data-driven models (e.g., forecasting, clustering/segmentation, learning-based