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, pumps, valves operated by a computer and the corresponding software Develop flow cells to connect various spectroscopic tools to the setup Create and validate reproducible automated workflows
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funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Leverage computer vision, smart
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that affects the measured data and translate the observations into mechanistic insight Lead a PhD student in development of a controlled flow system for in situ ATR-UV–Vis spectroscopy including
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translating these insights into Concepts of Operations (CONOPS) for navigation and energy management. The researcher will contribute to the architecture, management and use of the data platform supporting
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researcher who enjoys developing methods for complex biological data and working closely with experimental scientists. You meet the following criteria: a PhD, or a PhD close to completion, in machine learning
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scientists. You meet the following criteria: • a PhD, or a PhD close to completion, in machine learning, artificial intelligence, computational biology, bioinformatics, computer science or a closely related
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that prioritise student learning in the age of AI. Job requirements You hold a PhD in Educational Science, Learning Sciences, Social and Behavioural Sciences, Engineering Education, Human-Computer Interaction or a
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environments, spectral sensing in the visible and near-infrared (NIR) range to estimate sugar and starch content, chlorophyll levels, and plant water status, AI-based computer vision systems to monitor crop
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structure modeling in cancer immunotherapy design. Profile A — AI PhD in machine learning, computer science, computational science, or a related field. Strong experience with deep learning (e.g., PyTorch
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an inclusive, collaborative research culture. What we ask of you You are a curious and rigorous researcher who enjoys learning across disciplines. You can translate an ambitious methodological idea into a