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investigate new algorithmic principles that make learning agents adapt to non-stationary environments in an autonomous manner. The expected outcomes are new theoretical insights about the algorithmic roots
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Systems (DESS) research group and supervised by Professor Torben Bach Pedersen. Within Digital Energy, DESS has developed the award-winning FlexOffer technology, one of the few open, general, and scalable
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team. Significant software development experience in several key languages, e.g., Rust, C++, or Python (not MATLAB), algorithms, and machine learning is necessary as well as excellent communication
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hardware modification. The AI will learn and adapt the realms of the combustion modes and fine tune the performance for each while the engine is operated. Self-tuning, adaptive, control algorithms will be
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behaviour. This will include developing and using state-of-the-art image recognition algorithms to create digital twin models as well as statistical and machine learning methods for analysing large-scale
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, international team with flexible work organization and support of individual development. The group is involved in a variety of national and European projects and features a strong network of academic and
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developing 3D vision algorithms for object detection, recognition, and scene understanding to support planning and task execution in dynamic environments. Publishing research findings in leading international
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data Design algorithms for correlating low-level events into process-level attack models Contribute to joint framework development with TU/e on continual learning Collaborate with industry partners
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-tasks and language models. This position is part of SDU’s initiative to develop energy-efficient AI accelerators based on alternative model architectures that cannot be leveraged as efficiently
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these frameworks to develop specific formulations and solution algorithms for the design of congestion pricing schemes using classical transport models and quantify the equity-efficiency trade-offs for congestion