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TensorFlow or PyTorch Knowledge of neural networks – especially convolutional neural networks (CNNs) Understanding of image processing techniques – including filtering, edge detection, and image segmentation
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/Province Bremen City Bremen Website https://www.uni-bremen.de/en/the-martian-mindset Street Postfach 330 440 Postal Code 28334 E-Mail [email protected] martianmindset.applications@uni
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Website https://sede.udc.gal/services/electronic_board/EXP2026/008881 Requirements Research FieldPhysicsEducation LevelPhD or equivalent Skills/Qualifications PhD Additional Information Eligibility criteria
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following the national regulations (https://www.fm.gov.lv/en/s/taxes/ ). Eligibility criteria (requirements and necessary competences): The position is open to applicants who meet the following criteria
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Code70013StreetVOUTESGeofield Contact State/Province Crete City Heraklion Website http://www.elke.uoc.gr Street University Campus, Voutes Postal Code 70013 E-Mail [email protected] Phone +302810394568 STATUS: EXPIRED X (formerly
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-055StreetPoniatowskiego 15Geofield Contact State/Province Silesia City Katowice Website http://sum.edu.pl/ Street Poniatowskiego 15 Postal Code 40-055 E-Mail [email protected] Phone 322083686 STATUS: EXPIRED X (formerly
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the image-processing pipeline for the detection, tracking, and characterization of space objects. I.4. Gross monthly wage: 2.408,11 euros, corresponding to remuneration level 33 to the single remuneration
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/InstituteUniversity of OldenburgCountryGermanyGeofield Contact City Oldenburg Website https://www.uni-oldenburg.de/en/medicine/ Street Carl-von-Ossietzky-Straße 9-11 Postal Code 26129 E-Mail helge.meyer@uni
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available1Company/InstituteNaturalis Biodiversity CenterCountryNetherlandsCityLeidenPostal Code2333 CRStreetDarwinweg Contact City Leiden Website http://www.naturalis.nl/ Street Darwinweg 2 Postal Code 2333 CR STATUS
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relevant to physics, such as CNNs for image-based field prediction, Graph Neural Networks (GNNs), or Physics-Informed Neural Networks (PINNs) Solid grasp of numerical methods, partial differential equations