208 machine-learning-"https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" "UCL" "UCL" Master scholarships
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vision based object detection tools, and reinforcement learning techniques. Additional Information Benefits Monthly Maintenance Allowance: €1,309.64 Funding Entity: Instituto Superior Técnico (IST
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be duly proven at the time of hiring. 2; 3. Preferred requirements: Experience using Machine Learning algorithms. In-depth knowledge of Python and PyTorch. Previous experience collaborating
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. Estrela do Céu da Costa Neto. Address: Rua Dr. António Bernardino de Almeida, 400; City: Porto; Postal code: 4200 - 072 Contacts: Phone +351 222061000; Email: geral@ess.ipp.pt ; Website: https
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Requirements Research FieldBiological sciencesEducation LevelMaster Degree or equivalent Additional Information Website for additional job details https://www.cesam-la.pt/en/calls/research-grant-under
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goreti.pereira@ua.pt Requirements Research FieldChemistryEducation LevelBachelor Degree or equivalent Additional Information Website for additional job details https://www.cesam-la.pt/en/calls/research-grant-under
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 2 months ago
, reference no. 2023.18249.ICDT, financed by national funds through FCT/MCTES (PIDDAC Workplan: Development of methodologies and machine learning algorithms for the detection of anomalous behaviors in flow rate
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, Economics, Management, or related fields. [1] ; Be a student enrolled in a doctoral program in Computer Engineering or Computer Science - a requirement to be duly proven at the time of hiring. 2; 3. Preferred
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stage. 3. Preferential Factors Proven experience with decision support systems based on knowledge bases and machine learning. Previous experience in machine learning applied to dynamic systems or orbital
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to monitoring cracking in concrete bridges and viaducts through computer vision The work is supported by BRISA and will use as case study the viaduct over the Rio de Anços on the A1. Duration: 6 months Maximum
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machine-learning methods for sample segmentation and classification. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: The fellow will join the INESC TEC team within the LIBScan project, carrying