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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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-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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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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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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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
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Innovator Grant from the Novo Nordisk Foundation and carried out under the supervision of Professor Claus Elsborg Olesen. The project’s main objective is to optimize the RNA delivery platform through in vitro