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work jointly to integrate the two approaches into a single RL algorithm. While the work is mainly methodological and theoretically focused, our Aalto robot lab offers the possibility to evaluate the
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consumption on complex algorithmic or cognitive tasks. This project is part of the ELEVATE MSCA Doctoral Network (https://www.elevate-dn.eu/) and co-supervised by our partners at the university of
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comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures ( https://CGRlab.github.io/research/ ). About the research project We are open
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development of models and algorithms for simulation and optimization of decentralized, integrated energy systems. Such systems are characterized by high shares of renewable energies and increasing
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data analysis and algorithm development designed to determine the underlying factors contributing to health and disease, with a strong focus on discovering disease mechanisms at the level of their
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Working closely with partners at TUD and theoretical researchers on algorithm development, performance analysis, implementation, and experimental validation. To be qualified for this position
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optimization Experience with large-scale algorithm design and implementation Knowledge of decision making under uncertainty Ideally, some exposure to machine learning methods A passion for research
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several of these highly topical research projects and the societal transformation that follows with the development of AI-powered smart care solutions. The position is at the Department of Computer
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, interdisciplinary research environment with strong focus on mentoring and career development. Starting date upon individual agreement and preferably no later than 1st of April 2027. The fellowship period is three
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receiver architectures that enable power-efficient signaling at the maximum allowable spectral efficiency. The project will cover both algorithm development and experimental implementation. We offer an