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learning, as well as process-based models integrating thermal biology mechanisms. Funded by the ”Federal Ministry of Research, Technology and Space” (BMFTR), CSIDlab as part of the “Vector-borne disease
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learning, as well as process-based models integrating thermal biology mechanisms. Funded by the ”Federal Ministry of Research, Technology and Space” (BMFTR), CSIDlab as part of the “Vector-borne disease
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-language models, or diffusion models to simulate and design complex urban traffic environments? Do you want to shape the next generation of data-driven mobility intelligence with your creative ideas and
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sequencing, genome-wide data integration, statistical modeling, and hypothesis-driven experimental design, preparing them for leadership roles at the interface of molecular biology and data science. Your Tasks
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interdisciplinary PhD projects in the Simulation and Data Lab Digital Bioeconomy. Each project combines natural sciences with computational and data-driven approaches, focusing on topics such as plant carbon
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computational and data-driven approaches, focusing on topics such as plant carbon transport, microbial systems, or circular bioprocesses. You will contribute to developing and applying novel modeling strategies
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electrolysis and fuel cells (SOEC and SOFC). By combining numerical modeling with data-driven approaches, you will identify optimized operating conditions and strategies to improve both steady-state and dynamic
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modeling with data-driven approaches, you will identify optimized operating conditions and strategies to improve both steady-state and dynamic performance in fuel cell (biogas) and co-electrolysis
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/ Production Technology / Process Engineering / Preventive Wound Treatment / Anti-inflammatory and Antimicrobial Dressing / Burn and Surgical Wounds / Particulate Delivery Systems / Ex-vivo Models Where to apply
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Description TUD Dresden University of Technology, as a University of Excellence, is one of the leading and most dynamic research institutions in the country. TUD has established the Collaborative