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formulations as complementary model systems. Integrate experimental work with spin-diffusion modeling and other complementary techniques. Collaborate actively with AstraZeneca (model systems and analytical input
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University of Technology , where you will develop explainable AI models for personalized treatment planning in sports medicine and orthopaedics. You will work in a highly interdisciplinary environment
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focus on two main lines of research. The first concerns the modeling of general dark matter–electron interactions in detector materials. This will be achieved by combining methods from particle and solid
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explainable AI models for personalized treatment planning in sports medicine and orthopaedics. You will work in a highly interdisciplinary environment, collaborating with leading experts in AI, mathematics
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on two main lines of research. The first concerns the modeling of general dark matter–electron interactions in detector materials. This will be achieved by combining methods from particle and solid state
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numerical models and signal processing methods to detect and understand seismic events directly from communication signals in optical fibers — paving the way for a new class of communication-based seismic
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ocean environments, ensure safe and sustainable operations. Our activities are centered on numerical modelling (e.g. CFD, FEA, FSI, optimization, machine learning), but also include experiments and real
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amplifier performance. By combining advanced device measurements, empirical modeling, and power amplifier design, this project will generate new insights into the material, process, and design factors
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for self-deployable 6G networks in the edge continuum (EC-DEPLOY-6G) pioneers the use of large language model–driven agents to autonomously configure and deploy 6G and cloud functions. The project leverages
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into elementary particle physics from model building and Dark Matter to formal Quantum Field Theory. Organizationally we are part of the division of Subatomic, High-Energy and Plasma Physics within the Department