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successful candidates will develop fundamental theory and practical algorithms that move reinforcement learning closer to real-world systems. Position 1 will focus on trajectory-centric optimization
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theory and practical algorithms that move reinforcement learning closer to real-world systems. Position 1 will focus on trajectory-centric optimization, Position 2 on adaptive policies, and both will
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in quantum computing and algorithms for Life Science applications. Are you as excited about quantum technology and its future applications as we are? We are seeking a doctoral researcher to join the
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energy-intensive infrastructures. Implementing algorithms and simulation models using Python, MATLAB, Julia, or other suitable tools. Analysing simulation and project data, and validating the developed
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algorithms and data analytics tools, and share your results with experimental collaborators. The position is part of the Research Council of Finland project ELPH-ML ( https://research.fi/en/results