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We are offering a PhD student position in machine learning (ML) theory, focusing on new methods for training models with a limited amount of data. The student will be a part of a new NEST initiative
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build the sustainable companies and societies of the future. Operation and maintenance is a rapidly growing research area, as it is recognized as an important enabler for the business performance by
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focusing on: Quantum mechanical calculations using density functional theory. Mean-field modeling and Monte Carlo simulations for reaction kinetics. Theoretical spectroscopy By combining quantum mechanical
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and only areas close to the dams were regarded important to investigate dam safety. The objective of the research is to evaluate variations of tailing composition in terms of segregation and densities
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work together, cooperative autonomous systems emerge. These systems hold transformative potential in applications like robotics, environmental monitoring, smart transportation, warehouse automation, and
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application! This position is in a lab with an interest in many aspects of theoretical ecology (coexistence, niche theory, ecological networks, metacommunities, eco-evolutionary dynamics, and more), where you
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laboratory work and data analysis? Then this position may be right for you. While the focus will be on your research and personal development as a researcher, the position also provides opportunities
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. You enjoy combining experimental laboratory work with theoretical analysis and modelling. While your main focus will be the research project and your own development as a researcher, the position also
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This PhD position offers a unique opportunity to advance safe and transparent control for autonomous, over-actuated electric vehicles. You will work at the intersection of model predictive control
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third-cycle courses. The last day to apply is May 30, 2025. Project description and tasks: The PhD student will carry out research in the analysis of Partial Differential Equations as part of a project