193 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" Postdoctoral research jobs in Germany
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projects within the Clusters of Excellence ‘Machine Learning for Science’ and ‘Image-Guided and Functionally Instructed Tumor Therapies (iFIT)’. Requirements PhD in Bioinformatics, Computational Biology, or
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multimodal vision-language models for prompt-based 3D medical image segmentation Work with large-scale clinical CT datasets and scalable deep learning pipelines Validate models in close collaboration with
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: https://www.pisa.tum.de/pisa/jobs/ Für eine der PISA Hauptdomänen (Lesen, Mathematik, Naturwissenschaften) nach Wahl wird zum nächstmöglichen Zeitpunkt ein*e Postdoc gesucht. Nähere Informationen zur
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machine learning algorithms Strong communication skills and ability to work in interdisciplinary teams Fluency in spoken and written English We offer: A dynamic and interactive research environment as
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is expected. Background Information: Nat. Chem. 2015, 7, 105; Chem. Eur. J. 2019, 25, 4590; Angew. Chem. Int. Ed. 2019, 58, 418; Nanoscale 2021, 13, 19884. www.sengegroup.eu https://www.ias.tum.de
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) analysis • Research, development and implementation of deep-learning approaches • Network architecture search • Real-time image analysis • Establishing multi modal (video, thermography, acoustic, RFID
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Max Planck Institute for Extraterrestrial Physics, Garching | Garching an der Alz, Bayern | Germany | 27 days ago
Studies (CAS ) at the MPE invites applications for an Astrochemistry Postdoctoral on computational chemistry. A particular focus of the project is on utilizing machine learning techniques for molecular
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biophysics, computational biology, mathematics in the life sciences, computer science and machine learning with application to biological systems, and related areas. What we provide The CSBD provides fully
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to joint research activities, publications, and surveys. Requirements PhD degree (or near completion) in robotics, control, machine learning, or a related field; Strong publication record demonstrating
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on the true, astrophysical candidates is a computational needle in a haystack. To tackle these “big data” challenges, astronomers have begun to employ machine learning techniques. The application of machine