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, learning, visual literacy, collaboration, shared spaces, physical installations, user experiments. All details here: https://bivwac.fr/jobs/ Where to apply E-mail phd-26_bivwac@inria.fr Requirements Research
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https://phd.fbk.eu/calls/detail/artificial-intelligence-and-machine-learning-fo… Requirements Research FieldOtherEducation LevelMaster Degree or equivalent Additional Information Work Location(s) Number
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the groups of Dr Joe Forth, Dr Anthony Bradley, and Project Lead Professor Steve Rannard, applying your expertise in machine learning, cheminformatics, and soft materials to accelerate LAT design and
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application! We are now looking for a PhD student in Computer Vision and Learning Systems at the Department of Electrical Engineering (ISY). Your work assignments Your task will be to analyse and adapt vision
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hemocompatible coating strategies to improve membrane–blood interactions. - Model and optimize membrane performance using computational tools, machine learning, and artificial intelligence Work Plan - Synthesis
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testing data Development of machine learning models for battery health assessment and remaining useful life prediction Job Requirements: PhD degree in Electrical Engineering or related subjects. Expert
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on computer vision. The role involves developing and advancing novel algorithms for emerging challenges in computer vision, including continual learning and few-shot learning. The candidate is also expected
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. The successful applicant will develop a predictive pipeline using atomistic modeling and machine learning to identify optimal "seeds" for directing crystal growth, followed by rigorous experimental testing
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motivated candidate with a strong background in statistics and/or machine learning. Areas of particular interest include, but are not limited to: Causal Discovery and Causal Inference Extreme Value Theory
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segmentation, target detection and change detection along and across multiannual series of data. Methodologies like foundational models, machine learning, deep learning, multitask learning, enforcement learning