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contract 1.5–2 years, total 3–4 years) on deep learning for medical imaging. This DFG-funded project focuses on developing deep learning methods for medical and scientific imaging. The Professorship
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: A completed university degree (Master or equivalent) in computer science, data science, applied mathematics, physics, materials science, or a related field Prior experience in computer vision, deep
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neurons. Responsibilities and tasks This PhD project aims to develop, verify, and benchmark learning rules in networks of complex spiking neuron models in the application field of geolocalization: Building
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, and training methods - across multiple technological platforms - photonics, electronics, biological neurons. Responsibilities and tasks This PhD project aims to develop, verify, and benchmark learning
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the reference number 27697, via our online portal: Apply now via https://jobs.uksh.de/job/Kiel-PhD-%28mfd%29-Statistical-Genetics-Machine-Learning-Schl-24105/1279933701/ For more information visit: www.uksh.de
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train deep learning models using agricultural image datasets Collaborate with researchers at the University of São Paulo and agricultural domain experts Publish research findings in peer-reviewed journals
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acceleration of large-scale machine learning workloads Perform characterization and modeling of electronic and optical devices Develop hardware-aware machine learning models incorporating electronic and optical
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on healthcare, research, and teaching, we bear a unique responsibility – ideally with you on board! The position is based at the Institute of Medical Informatics within the research group Machine Learning
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: Investigate and design optimal computing and communication architectures for hardware acceleration of large-scale machine learning workloads Perform characterization and modeling of electronic and optical
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on the ERC project Learning Matters!. Task You will implement learning mechanics in soft matter, specifically in biocompatible hydrogels. Your hydrogels will form the walls of a synthetic vein network that can