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—especially GaN devices—using pre-metallized AlN carriers. The aim is to understand, optimize, and scale bonding processes for both small and large GaN chips. Planned Thesis Work will include both literature
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theoretical evolutionary biology, including optimal control theory, life history modelling, adaptive dynamics, and population genetics. This position is part of the interdisciplinary consortium
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approaches and optimization System dynamics modelling Energy system resilience and risk assessment The research will heavily involve mathematical model development and the use of specialised software
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quantitative predictions testable against empirical data from diverse ecological contexts. We use methods from theoretical evolutionary biology, including optimal control theory, life history modelling, adaptive
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of their pigments, through the optimization of their production and application, to the comprehensive ecological and economic evaluation of the resulting solutions. The successful applicant will be employed by
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applicants are expected to have a strong experience in hydrodynamic simulations and radiation transfer, preferably with interest in applying novel computational techniques, in order to optimize
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) in computer science, mathematics or statistics, with an excellent publication record. Solid research experience in one or more of the following topics is expected: Graph neural networks Optimization
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. Machine-learning-enhanced quantum simulation, including optimization of measurement protocols, regression of observables from single-shot data, and data-driven characterization of complex quantum phases
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, use of forest simulators through a simulation and optimization framework. The research can focus on how to improve the use of decision support tools by private forest owners; focus on the trade-offs and
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through continuous collaboration with these organizations. The dissertation work should optimally connect with the following thematic areas: Designing and studying AI control solutions and human–AI agent