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required to complete PhD courses corresponding to 30 ECTS, gain experience with teaching or other forms of knowledge dissemination and complete an external research stay outside of Aalborg University
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candidate will have expertise in several of the following areas: • Strong knowledge of ocean circulation, physical oceanography, and numerical ocean modelling. • Experience with regional ocean models
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learning frameworks (e.g., PyTorch). Core Knowledge: A strong foundation in machine learning and/or computer vision. The candidate should have a specific interest in test-time adaptation, autonomous AI
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knowledge that is both academically relevant and practically meaningful. The project will explore how collaborations across the value chain emerge, how partnerships are organized and established, and how
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to teaching or other forms of knowledge dissemination within the department. Your competencies You hold a Master’s degree in forensic science, chemistry, chemical engineering, analytical science, data science
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experience with teaching or other forms of knowledge dissemination and complete an external research stay outside of Aalborg University, preferably 3-6 months at a foreign research institution. Who we
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, together with experience in uncertainty quantification, Bayesian inference, or probabilistic risk assessment. Background knowledge in fault detection and diagnosis, condition monitoring, or health management
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-principles physical knowledge with data-driven learning to enable continuous, autonomous system oversight. Research objectives The project pursues two interconnected research directions: Continuous
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Electrical Engineering, Energy engineering, or a related field (or about to complete) with a strong background in power electronics. Knowledge and experience in power electronics, electronic packaging, PCB
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institution is encouraged, for example through shorter or longer research stays at other research institutions. In addition, PhD students are expected to build knowledge of external research funding