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amounts of clinical data are needed to train useful AI algorithms. However, patient data are person-sensitive, and only selected individuals can obtain access, which can be a significant roadblock for
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better and faster decisions when assessing funding applications, ensuring the efficient and unbiased elimination of poor applications? This question can be addressed through training algorithms on past
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electricity price signals, demand-response mechanisms, and time-of-use optimization. AI-Driven Optimization using Reinforcement Learning: Apply RL algorithms to develop and train agents that optimize power
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Post doc position in theory of machine learning at Department of Computer Science, Aarhus University
is on understanding and improving the performance of classic learning algorithms, in particular Boosting and Bagging, both in terms of speed and generalization capabilities. The project also allows
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biased, outdated, or sensitive data? That's where the project TRAI comes in. This research project aims to develop machine unlearning algorithms to selectively erase specific knowledge from trained AI
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programming Creating their own mechanical designs, implement and test them accordingly, Implementation of control algorithms on physical experiments. In addition, the candidates are expected to contribute with
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, creative labor, and cultural and communicative work, to the use of technologies, artificial intelligence, and algorithmic management. These focus areas are suggestive, and applications that address other