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, experimentally grounded workflow for rapid microstructure-property optimization in steels. The PhD student will play a central role in this interdisciplinary initiative. They will: Develop and apply machine
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Program for Collaborative Doctoral Projects, please visit: https://go.fzj.de/Collaborative-Doctoral-Projects . We are offering a Collaborative Doctoral Project (PhD Position) – Computationally-Enabled
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Dortmund, we invite applications for a PhD Candidate (m/f/d): Multidimensional Omics Data Analysis You will be responsible for Prediction of metabolic activity in complex microbial communities, leveraging
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Infrastructure? No Offer Description Work group: IAS-9 - Materials Data Science and Informatics Area of research: Promotion Job description: Your Job: The PhD project is methodologically independent, with
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Max Planck Institute for Extraterrestrial Physics, Garching | Garching an der Alz, Bayern | Germany | 25 days ago
Physics invites applications from talented, ambitious graduating MSc students for a PhD position on Exploring Supernova Remnants in eROSITA and Multiwavelength Data (m/f/d) Job Code: 16/2025 The PhD project
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research together with cutting-edge materials science and physics. Depending on your background you will work collaboratively on the following tasks with either with a stronger model-development or
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America, Asia or Africa). The PhD process will be accompanied by integration into TUM’s School of Life Sciences or School of Management and participation in the related Graduate School training programs
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, computer science, medicine, pharmacology, and physics. ISAS is a member of the Leibniz Association and is publicly funded by the Federal Republic of Germany and its federal states. At our location in
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Project (PhD Position) – AI-Guided Design of Scaffold-Free DNA Nanostructures Your Job: The field of structural DNA nanotechnology holds a great promise for the realization of all-DNA building blocks with
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excursions. The PhD student will work on analysing the reliability of paleomagnetic records, deriving uncertainty estimates, and building time-dependent models of the paleomagnetic field evolution using a