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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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of geological archives with knowledge of modern Arctic climate and oceanography. Who are you? You have a MSc degree in geology, geoscience, environmental science, oceanography, paleoceanography or a related field
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, Chemical Engineering, Physics, Chemistry. Knowledge of dairy systems and/or relevant characterisation methods will be considered an advantage. Our group and research- and what do we offer? The group is a
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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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) Strong knowledge of classical and modern control theory Experience in designing control strategies for energy-efficient fluid power systems Experience with modelling and control of hydraulic displacement
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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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with colleagues. Proficiency in written and spoken English is required. Knowledge of Danish is an advantage in relation to teaching and data collection, but it is not a requirement. Please note that non
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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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-principles physical knowledge with data-driven learning to enable continuous, autonomous system oversight. Research objectives The project pursues two interconnected research directions: Continuous