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Skills/Qualifications Completion of a Master's degree in the field of Deep Learning applied to images and video. Scientific publications in the area of attribute recognition in images or medical imaging
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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics
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/A (Logic and Philosophy of Science), research project "Critical History of Deep Learning". DEADLINE: February 2nd 2026, AT 1:00 P.M. CET Where to apply Website https://www.unive.it/data/50068/?id=2026
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efficient deep learning and support to teaching and outreach on sustainable and multimodal AI. Where to apply Website https://www.unimore.it/ Requirements Additional Information Eligibility criteria Eligible
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be focused on deep learning based dose accumulation for pancreas patients undergoing MR-guided radiotherapy under the direction of Drs. Neelam Tyagi and Harini Veeraraghavan in collaboration with a
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solutions that enhance ecological monitoring, improve resilience planning, and promote sustainable resource management. Development of a Detection Transformer through Attentive Deep Learning and Explainable
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Qualifications* Must be enrolled in a Rackham Graduate Program (https://rackham.umich.edu/programs-of-study/ ) Must be in a biological, chemical, or physical science discipline. Must hold an advanced degree or be
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(pre-processing, filtering, feature extraction in the time, frequency, and time-frequency domains). Development and validation of machine learning and deep learning models; integration and analysis
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skills: Good knowledge of ML/AI based techniques to develop fast surrogates (deep neural networks) and capability to develop own efficient model learning schemes (deep learning techniques, representation
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, storage, and local electricity grids. A key goal is to translate methodological innovations in deep learning into practical tools for sustainable urban energy systems, supporting applications in forecasting