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To boldy go where no man has gone before. Job description The PostDoc position is part of research of prof. Bernd Rieger and prof. Sjoerd Stallinga, in which we target (super-resolution) microscopy
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Class Acad Prof and Admin Add to My Favorite Jobs Email this Job About the Job The individual will perform multi-dimensional physiological and biomarker analyses collected from multiple datasets from
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algorithms for data processing, assisted or automated flaw detection, 3D EM solvers, and synthetic aperture radar (SAR) focusing will be used to refine spatial resolution. Applicants should have, or expect
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• Develop, consolidate, and optimize fMRI and EEG neurofeedback algorithms. • Design, integrate, and test standalone neurofeedback software (software suites for clinical environments). • Contribute
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EU MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization of
(MSCA) doctoral network led by Prof. Cecilia Persson, Uppsala Universitet. Print4 Life – Advanced Research Training for Additive Manufacturing of the Biomaterials and Tissues of the Future https
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on rapid and accurate quantification of disasters using remote sensing and space geodesy. They will also advance InSAR processing algorithms to optimise change detection capability in Southeast Asia, where
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logical choice for its staff and students. YOUR TASKS Job description The “AI for Energy” team (AI4E), led by prof. Chris Develder and prof. Bert Claessens at the Internet and Data Lab (IDLab) at Ghent
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Institution: Technical University of Munich (TUM), Germany – 12 months Industrial Partner: GIM Robotics, Finland – 24 months Supervisors: Prof. Achim Lilienthal (TUM), Dr. Jose Peralta (GIM Robotics) Target
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 6 days ago
management from water and energy sensors; Development of algorithms to improve data collection pipelines from water and energy sensors; Analysis of water and energy datasets using AI techniques, including
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. The project aims to address the challenges in pooling inference, by developing and implementing either exact or asymptotically exact Monte Carlo algorithms in collaboration with the Department