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you are suitable for this position. CV (max 2 pages) A certified copy of your master's degree and your course grades. Copies (or drafts) of your master's thesis and other documents, such as publications
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EU MSCA doctoral (PhD) position in Materials Engineering with focus on mechanistic study of biodegra
. Your application must include: A short cover letter (up to 1 page) in which you describe yourself, why you want to do a PhD and why you are suitable for this position. CV (max 2 pages) A certified copy
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the formation of double-strand breaks, copy-number alterations, and other structural genome changes. Our group combines cutting-edge genome biology approaches with quantitative, multi-omics analyses to uncover
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transport, microbial systems, or circular bioprocesses. You will contribute to developing and applying novel modeling strategies, AI-enhanced simulations, and computational workflows to explore biological
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Your Job: You will work as a doctoral researcher within one of four interdisciplinary PhD projects in the Simulation and Data Lab Digital Bioeconomy. Each project combines natural sciences with
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through simulation Utilize advanced CMOS technology nodes (28nm, 22nm, and below) Automate the design and layouts using Skill programming Develop behavioural models for circuit verification Contribute
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Your Job: Modeling and characterization at molecular level of selected biological processes by performing classical molecular dynamics, and employing enhanced sampling methods and machine learning
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particular: Investigation of reaction mechanismus using density functional theory and statistical thermodynamics Kinetic modeling using mean-field models and transition state theory Conducting MD simulations
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suitable for part-time employment. Starting date: 17.10.2025 Job description: Design, develop and apply an flexible and integrative multiscale FWI using GPU-accelerated spectral-element simulations (Salvus
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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