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only) with a motivation letter, CV, copy of degree certificate, transcript of grades (i.e. the official list of coursework including your grades), and proof of English language skills by January 6, 2026
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Identify and evaluate online‑measurable proxies (e.g., froth‑camera metrics, X‑ray fluorescence) for ore microstructures; conduct offline μXRF/TIMA analyses to simulate various XRF‑system configurations
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complex materials with tailored properties using high-throughput simulations, data analytics, and material characterization. Benefit from strong connections to top research infrastructures like the Jülich
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demands. To break this bottleneck and cut simulation time by orders of magnitude, you will design and implement surrogate models that learn the behavior of full‑physics codes using modern machine‑learning
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are foreseen, applicants must be ready to travel Applicants must be eligible to enroll on a PhD program at TU Dresden (see https://tu-dresden.de/ing/maschinenwesen/postgraduales/promotion?set_language=en
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management (RDM) system. The goal is to create an overarching data space for the RTG that integrates various experimental techniques and simulation methods. This includes concepts for ensuring data quality
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opportunities within ScaDS.AI. Tasks: foundational and applied research in LLMs for traffic engineering, with particular focus on mobility knowledge benchmarking, simulation of human mobility behavior, simulation
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institutes at TU Clausthal, hosting several research groups. For more information about the ISSE Institute, please visit our website: https://www.isse.tu-clausthal.de/ Your responsibilities include: Research
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evaluation of efficient training and reconstruction pipelines involving deep learning models Find required initial conditions for LWFA simulations which yield the reconstructed observed electron buch shapes