27 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"SUNY" PhD positions at Aalborg University
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Engineering, Science and Systems (DESS) research group focuses on data-intensive systems, spatio-temporal data management, data analytics, and applications of machine learning, with applications in digital energy and
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The Department of Electronic Systems at The Technical Faculty of IT and Design invites applications for a PhD stipend in the field of Safe Learning Based Control for Autonomous Robots in Dynamic
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At the Technical Faculty of IT and Design, Department of Sustainability and Planning, a position as a PhD student in Problem-Based Learning (PBL) and Sustainable Education at the Aalborg Centre
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This PhD explores light estimation and synthetic relighting of real scenes by combining generative deep learning with computer graphics. It aims to reduce issues such as hallucinations and temporal
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combines multimodal data sources, physical models, and advanced machine learning to create new forecasting and communication tools. The lab is looking for candidates for the following two stipends: Stipend 1
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Engineering, Machine Learning, Artificial Intelligence, Computational Linguistics, or a related field) • Strong programming skills (e.g., Python) • Strong skills in machine learning, deep learning and modern
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PhD from the University of Nantes in France. He has worked 10 years at the university of Aalborg focusing on the development of statistical methodology for application in machine learning and
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candidate is expected to hold: A master degree in biomedical engineering or computer science, Excellent programming skills (Python). Experience with data curation, large-scale datasets, and machine learning
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electrical energy storage systems; energy management systems. Experience with data processing, statistical analysis and machine learning techniques is an advantage. Knowledge with Mathworks suite, C/C++ and
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of these materials. Implementation of artificial intelligence (AI) and machine learning (ML) to establish the connection between the existing models and material data (both literature and the baseline established in