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amphibians and other aquatic vertebrates. Using advanced imaging, neurophysiological, and molecular biological methods, we investigate the olfactory system. Furthermore, we use the olfactory network as a model
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the opportunity to contribute to collaborative efforts at the interface of data science, imaging, and materials research. You will strengthen the data science and machine learning activities of the IAS-9 with
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, ideally with knowledge of Drosophila genetics and live imaging the applicant should be able to relocate for 6 months to our collaborator in Chile, where they will develop and optimize novel metabolite
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the polyamine metabolism pathway by combining methodologies such as crystallographic fragment screening, imaging of protein complexes, and studies of cellular polyamine metabolism. The goal is to create a
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, ideally with knowledge of Drosophila genetics and live imaging the applicant should be able to relocate for 6 months to our collaborator in Chile, where they will develop and optimize novel metabolite
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domains are e.g., signal-/image processing, artificial intelligence and machine learning. Tasks: research and development in designing and programming field programmable gate arrays (FPGAs) for accelerating
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Max Planck Institute for Extraterrestrial Physics, Garching | Garching an der Alz, Bayern | Germany | 25 days ago
Job Code: 16/2025 Job Offer from July 24, 2025 The Max Planck Institute for Extraterrestrial Physics (MPE) in Garching is a world leader in the field of ground-based and space-based experimental
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industrial processes. Your research will drive a paradigm shift in how TES systems are modelled, integrated, and controlled within industrial settings. You will develop novel, adaptive, physics-informed models
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Max Planck Institute for Extraterrestrial Physics, Garching | Garching an der Alz, Bayern | Germany | 26 days ago
Job Code: 16/2025 Job Offer from July 24, 2025 The Max Planck Institute for Extraterrestrial Physics (MPE) in Garching is a world leader in the field of ground-based and space-based experimental
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materials systems at the molecular level with machine learning. The PhD Student will undertake a study analysing mass spectral imaging data streams in real time using machine learning workflows. A pathway for