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: Collect and critically analyze relevant literature Build and analyze models for predicting dynamic trickle bed reactor operations, including material and energy balances, reaction kinetics, mass and heat
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provide GPU computing for machine learning on Aalto's Triton high-performance computing cluster, alongside industry-standard circuit simulation tools (Cadence, Synopsys, etc.) on our own computing cluster
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for trajectory-centric stochastic optimization under non-ergodic dynamics; Implementing practical RL algorithms that optimize long-term performance of individual agents; Developing change detection algorithms
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to exoplanet discovery and space‑weather environments. You will work in Aalto’s High‑Performance Computing Lab within the PEAS (Population of Exoplanets around Active Suns) consortium alongside leading teams in
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outputs. The research methods will include experiment design, software implementation, running deep learning experiments on high-performance computing infrastructure, and evaluation using objective metrics
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-Sailors) network, funded by the Marie Skłodowska-Curie Actions (MSCA) Doctoral Network Programme. The E-Sailors network is designed to train the next generation of Electric solar wind sail (E-sail
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outputs. The research methods will include experiment design, software implementation, running deep learning experiments on high-performance computing infrastructure, and evaluation using objective metrics
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performance analyses for backscatter-assisted non-line-of-sight positioning Applying detection and estimation theory to questions such as detectability, identifiability, positioning accuracy, and robustness
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uncertain sensing, with emphasis on safe operation among people, socially aware perception and behaviour prediction, and semantic/dynamic environment understanding. Our research environment offers close
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Aalto University is a place where science and art meet technology and business. We are building a sustainable future by conducting high-quality research and educating future experts. The Aalto