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in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages, PyTorch Familiar with foundation models (vision large models or multi
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real-time during behavioural tasks Integrating virtual reality paradigms with behaviour and imaging to assess decision-making processes Exploring potential therapeutic interventions for NMDA receptor
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | about 4 hours ago
of critical thinking Experience with photonics, fluorescence, imaging or biochemical methods is helpful Our offer Highly motivated team Flexible working hours World-class institute with outstanding
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15.04.2025, Wissenschaftliches Personal The Lab for Artificial Intelligence in Medical Imaging (www.ai-med.de) is inviting applications for a fully funded PhD position in interpretable machine
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, you need to develop and apply methods that enable the recognition of phenological stages of allergenic plants on images. Then, the phenological development of these species shall be modelled based
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and IT develop innovative radiopharmaceuticals and novel tools for functional characterization, improved imaging and personalized treatment of tumors. The Department of Department Life Science
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the development of the optical setup, nanofluidic systems, and image processing. Additionally, you will be actively involved in experimental investigations to tackle key questions in life sciences and collaborate
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document fossil turtle specimens Process and analyze digital CT datasets of cranial and neurological structures Carry out geometric morphometric analyses to study shape variation Conduct eco-morphological
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Course location Hamburg Description/content The Cluster of Excellence "CUI: Advanced Imaging of Matter", which is funded by the German federal and state governments, combines projects in physics
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and performing laboratory (wind-wave facility) experiments, using state-of-the-art imaging techniques developing computational codes to process and understand large experimental datasets (e. g., image