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Brown? Brown University is a leading research university that is distinct for its student-centered learning and deep sense of purpose. Our students, faculty, and staff are driven by the idea
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https://pubmed.ncbi.nlm.nih.gov/36596869/ Research area: Cancer biology Keywords: lymphoma, CLL, lncRNA, microenvironment Funding of the PhD candidate: Part-time salary (min. 0,5 FTE) on EHA grant/AZV
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National University of Science and Technology POLITEHNICA Bucharest, Pitesti Branch | Romania | 26 days ago
(machine learning, deep learning); adaptive control and algorithmic optimization; integration of AI models in embedded systems and software platforms. APPLICATION Before applying, all candidates are invited
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-of-the-art methods, datasets, and challenges Proven experience with: Video data processing for learning and inference Deep learning architectures for video analysis Python programming and PyTorch framework
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and development of perception stacks for autonomous mobile systems in general in any field Machine learning/deep learning experience applied to perception and any experience with deep Learning
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with the centre’s user partner Kongsberg Satellite Services (KSAT). We are therefore seeking someone with a strong interest and competence in deep learning. Working environment: The project will be done
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using deep learning or causal learning methods. Candidates must have solid experience with large spatial and temporal datasets, large model manipulation, and HPC. The candidate must also have experience
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) Experience in deep learning algorithms is a plus Ability to work in a highly international team and interdisciplinary project applicants are expected to have excellent language skills in English Opportunity
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artificial intelligence and/or machine learning, digital health, wearables, etc. Contribute to Epidemiology curriculum development, teach epidemiologic methods as part of the Epidemiology PhD methods sequence
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Your Job: Join our team as a dedicated scientist and contribute to our exciting research projects. Our work focuses on models and algorithms for supervised and unsupervised learning. We devise deep