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monitor (at runtime) their quality and performance. The position involves close collaboration with end-users at the Copenhagen Metro and Everllence, where you will apply co-design and co-creation methods
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written and spoken Willingness to engage in interdisciplinary collaboration and fieldwork Advantageous: Knowledge of bat ecology and species identification Experience with machine learning or automated
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opportunity to join the ERC-funded project “ALPS - AI-based Learning for Physical Simulation”. Expected start date and duration of employment These are 1–year positions from 1 May 2026 or as soon possible. Job
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CFD workflows and Lagrangian particle/cell tracking to extracting actionable insights with statistical learning and AI/ML—ultimately enabling more robust scale‑up, smarter process control, and faster
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for the treatment of PFAS contaminated water. The postdoc will work collaboratively within the research groups of Assoc. Prof. Bekir Engin Eser, Assoc. Prof. Leendert Vergeynst and Assoc. Prof. Kasper Vita Kristensen
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The Daasbjerg research group at the Department of Chemistry, Aarhus University, is seeking a candidate for a 31-month postdoctoral position. This position focuses on AI/machine learning to develop a
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10 research sections. We broadly cover digital technologies within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning
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research results in high-impact scientific journals. Your profile We are searching for a highly motivated candidate who has A PhD in soil science, agronomy or similar Collaborative skills and ability
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research results in high-impact scientific journals. Your profile We are searching for a highly motivated candidate who has A PhD in soil science, agronomy, biology or similar Collaborative skills and
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standardisation and faster commercialisation. Join this exciting academia–industry collaboration advancing next-generation HVDC subsea infrastructure to accelerate the energy transition. Project context and key