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competences within computational modelling, optimization and integration of thermal energy storage technologies – such as large water pits and phase change material storage. You will work with colleagues, and
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mathematical foundation of machine learning models. You will be responsible for developing scientific machine learning methodologies enabling new approaches for solving machine learning problems including
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. Our research is rooted in basic research and centres on mathematical models of the physical and virtual world, as a basis for the analysis, design, and implementation of complex systems. We focus
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Foundation Models initiative . The proposed starting date is 1 September 2025 or soon thereafter. The appointment will be made for a term of three years at a competitive salary and will follow the PhD study
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components are in use. More specifically, the PhD position will look towards connecting different advanced software tools (of multi-physics and data-based models) simulating the metal AM process
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advancement and interdisciplinary collaboration, dedicated to solving the complex challenges of today's rapidly evolving engineering landscape. In line with our commitment to excellence and our mission
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Project MYC is a critical transcription factor in most cancers, where it cooperates with many different protein complexes to activate diverse pro-tumorigenic pathways. Together with a senior postdoc in the
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research focus will include some of the following topics: Advanced sensor fusion and multimodal AI models for robotic intercropping. Self-supervised learning will generate multimodal agricultural pre-trained
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with the aim of making it a practical computational tool in the near term. The project will focus on Gaussian Boson Sampling (GBS), which is a specialized photonic quantum computing model with
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of classical computers will require thousands, or even millions, of qubits. This creates a major engineering hurdle: interconnect complexity. Each qubit needs dedicated control and readout lines, which