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1. A PhD degree in a relevant area, e.g., Chemical Engineering, Control Engineering, Computer Science, Systems Engineering, etc. 2. The candidates should have strong math, modeling, programming, and
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The successful candidate will work with Asst. Prof. Shen Shuting on combinatorial inference under a project on "Post-learning inference for near-optimal discrete structures". The main
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Take the next step in your research career at the intersection of digital twins, multi-timescale system optimization, and integrated Power-to-X systems with special focus on the electrical
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investigate out-of-equilibrium dynamics in high-dimensional disordered systems (including models relevant to machine learning and optimization) by characterizing the fixed points (metastable states, attractors
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energy systems and datasets. Validate and benchmark the developed models using building measurements, physics-based simulations and energy-system optimization models. Investigate how tabular foundation
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developing learning-based perceptive approaches. You will develop learning and/or optimization based approaches to multi-robot path and motion planning and coordination, integrating multi-modal perception and
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well as to the geometric optimization of the separator, including the swirl generator. Mandatory requirements PhD in mechanical, naval or aerospace engineering, or a related field. Proven experience in numerical simulation
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, and optimize multimaterial LPBF processes, integrating in-situ monitoring, process parameters, material combinations, and resulting microstructural and structural outcomes. Design and optimize
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and highly interdisciplinary research environment, bringing together expertise in theory, nanoparticle science, OLED technology, and organic chemistry. You will work closely with two ongoing PhD
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related fields). Holding a PhD degree, obtained upon completion of doctoral studies. Programming skills (Matlab, Python, etc.). Ability to work with large datasets. Knowledge of basic statistics. Ability