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, Position 2 on adaptive policies, and both will 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
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and GWAS methodology Machine learning approaches for high-dimensional data Algorithm development and evaluation Applications of AI in biomedical research This project is suitable for students with an
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and GWAS methodology Machine learning approaches for high-dimensional data Algorithm development and evaluation Applications of AI in biomedical research This project is suitable for students with an
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-standard tools such as Matlab Simulink to connect with existing control systems and support SIL/MIL simulation, and the resulting algorithm must run efficiently within the CPU constraints of an embedded
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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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mathematics, statistics, and algorithm design * Excellent problem-solving skills with a creative, research-driven mindset * Demonstrated ability to work collaboratively in team-oriented environments * A
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languages ( e.g. , Python, C++) for algorithm development, simulation, and data analysis Background in real-time signal processing ( e.g. , time-series data, filtering, feature extraction
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as healthcare, cyber-physical systems, and foundation models, including large language models. Depending on the candidate’s interests and background, the project can focus on algorithm design
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Liège. Your tasks in detail: Develop an event-driven learning algorithm for latent reasoning that sparsely updates network state and parameters in complex reasoning scenarios. Explore hardware
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not mandatory. Specific Technical Requirements: Proficiency in one or more programming languages, such as Python, MATLAB, C/C++, or Java, with a strong emphasis on scientific computing and algorithm