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, including finite-element simulation and topological optimization of light guidance in HCFs, and numerical simulation of thermo- and fluid dynamics under fiber-drawing processes. Apart from the main tasks
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. Our research integrates expertise from machine learning, optimization, control theory, and network science, spanning diverse application domains such as energy systems, biomedical systems, material
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is expected that they will actively and creatively develop and optimize the detailed methods to pursue the overall project goals and, after a training period, independently analyze genomic data using
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systems. This PhD project, part of a national initiative, aims to use AI to design and optimize thermal interface materials (TIMs). It combines machine learning, materials informatics, and experiments
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computing, with a focus on performance analysis, development, and optimization of scientific simulation codes. The work involves applications in plasma physics, computational fluid dynamics, and molecular
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mathematics, such as extreme value theory, inference for stochastic processes, optimization theory, and/or Monte Carlo simulations. Experience in obtaining research grants in national and/or international
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candidate will work fulltime on the above-outlined research project. It is expected that they will actively and creatively develop and optimize the detailed methods to pursue the overall project goals and
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contribute to the activities including TES unit development, laboratory testing and techno-economic analysis to identify optimal integration opportunities. Cooperation with industrial and academic national and
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control for medical robotics in the context of cardiovascular technologies. The goal is to innovate control systems for optimized interaction of soft cardiovascular pumps and wearable biofeedback systems
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the optimal design of social support systems. The PhD position primarily concerns the part of the program that studies how AI changes the organization of work and employees. The program currently includes 12