263 machine-learning "https:" "https:" "https:" "https:" "RAEGE Az" Fellowship scholarships
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: Department of Mechanical Engineering Scholarship Theme: Management and maintenance of DEM workstations, servers and computer network. Objectives Research and development of methodologies that allow optimizing
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on the applicants' enrolment in study cycle or non-award courses of Higher Education Institutions. Preference factors: Experience in musical audio machine learning frameworks, advanced knowledge in music theory, and
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joining the team, please, visit the link below for more information. For any inquire, please contact the PI: pedross@usal.es Where to apply Website https://ibfg.usal-csic.es/empleo.html Requirements
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programme Reference Number AE2025-0567 Is the Job related to staff position within a Research Infrastructure? No Offer Description Portuguese version: https://repositorio.inesctec.pt/editais/pt/AE2025-0567
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programme Reference Number AE2025-0509 Is the Job related to staff position within a Research Infrastructure? No Offer Description Portuguese version: https://repositorio.inesctec.pt/editais/pt/AE2025-0509
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tools, with particular focus on multi-threaded and distributed scenarios. Experience with observability tools, particularly OpenTelemetry. Solid knowledge and experience in machine learning, deep learning
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Wood Fellowship (http://grantwood.uiowa.edu/fellowship) program at The University of Iowa, which supports one-year fellowships in the arts, is currently seeking a candidate appointed through the
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. Where to apply Website https://aunicalogin.polimi.it/aunicalogin/getservizio.xml?id_servizio=1079 Requirements Additional Information Eligibility criteria Oral test aimed at ascertaining candidates
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approaches for binarized network models, identifying their strengths, limitations, and applicability within privacy-focused machine learning frameworks. Special attention will be given to evaluating
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benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: ● Research and develop novel reliable deep learning computer vision algorithms for the detection and quantification of GIM lesions