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. The position is a two-year role and funded by a DFF research project (‘Safeguarding Users’ Cognitive Autonomy in Human-AI Interaction’). Research objectives This project develops methods to detect changes in
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carbon, nitrogen, and water flows in agroecosystems. A solid background in uncertainty quantification, applied statistics, Bayesian calibration, and Monte Carlo simulations. Strong skills in scientific
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-AI Interaction’). Research objectives This project develops methods to detect changes in users' cognitive load and designs digital interventions that support cognitive autonomy, people's ability
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informative but also pose significant privacy risks. Your work will focus on developing and studying privacy-preserving methods, such as differential privacy, Bayesian privacy, federated learning and synthetic
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grant project “Driving innovation in crop resilience through Comparative QTLomics.” The selected candidate will contribute to five main objectives: 1. Apply large language models (LLMs) to collect
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, such as multi-objective optimization, model predictive control, mixed-integer optimization, stochastic optimization, energy management, or production scheduling. Good knowledge of integrated energy
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implementation or comparable tools. Experience with one or more relevant methods, such as multi-objective optimization, model predictive control, mixed-integer optimization, stochastic optimization, energy
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strategies and market positioning. Job description The successful candidate is expected to contribute to the overall objectives of the project by being involved in design, implementation, data collection, and
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researchers and accompanying families, including relocation service and career counselling to expat partners. Read more here . Please find more information about entering and working in Denmark here . Aarhus
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is clearly connected to Deep Learning and Computer Vision, and you can demonstrate experience with semantic segmentation, object detection, and generative AI models. You have solid skills in