242 image-processing-and-machine-learning "UCL" Postdoctoral positions in Netherlands
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researcher to work on systems theory for energy-based learning. What are you going to do? As a postdoctoral researcher, you will develop and analyse new algorithms for energy-based learning of complex systems
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, with a particular focus on the iron and steel sector. By using TROPOMI observations with advanced machine learning techniques, the project will provide independent information on emission patterns and
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Join TU Delft to uncover the biology of cellular robustness in an industrial yeast process and translate your discoveries into innovative genome engineering strategies. Job description You will join
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processing, machine learning, computational imaging, and clinical translation. Beyond your individual research contributions, you will serve as a technical coach for the PhD researchers, helping to align
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(adaptive) imaging strategies and reconstruction. The research combines ultrasound physics, signal processing, machine learning, computational imaging, and clinical translation. Beyond your individual
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nucleation pathways related to different emissions characteristics at the engine exhaust. The collected data would be helpful to validate the microphysics models for ice nucleation process, hence clarifying
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data would be helpful to validate the microphysics models for ice nucleation process, hence clarifying the different nucleation pathways for ice formation. You will collaborate with other researchers
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Are you fascinated by atomic-scale control of materials for future semiconductor and energy technologies? We are looking for three ambitious postdocs to work on advanced atomic layer processing
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Are you interested in (re)entering biotech research? This position supports a non-linear career path with a focus on implementing process analytical technology into bioprocesses. Job description
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theoretical challenges motivated by astrodynamics, optimisation, control, scientific machine learning, mission design and autonomous systems, translating these into new research directions in deep learning