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Condensed Matter with focus on resolving the question how material structure and composition control properties and dynamics of electronic states on ultrafast timescales, and how this paves the way for novel
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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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Your Job: Digital methods for inverse materials design are essential to efficiently create new, sustainable and recycling-adapted structural metals. Alloys with a reduced number of elements, so
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batteries (RFB), enabling affordable and durable long-duration energy storage. The approach is to use hierarchical structures, i.e. complex material layers that can be optimized to specific battery
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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: Digital methods for inverse materials design are essential
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physics, material science, electrical engineering or a related discipline In accordance with the European Union’s funding rules for doctoral networks, applicants must NOT yet have a PhD Familiarity with
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Your Job: As a PhD candidate you will develop and deploy an artificial intelligence (AI) driven approach to streamline high-throughput experimentation (IMD-3: Institute of Energy Materials and
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LINK: TBD Research field: Biomaterials / Biomaterials Synthesis / Nanochemistry / New materials: oxides, alloys, composite, organic-inorganic hybrid / Nanoparticles / Pharmaceutical Chemistry
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Inverse Design of Perovskite-Based Materials for Photovoltaic Devices Your Job: As a PhD candidate, you will develop and deploy an artificial intelligence (AI)-driven approach to streamline high-throughput
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– from the modeling of material behavior to the development of the material to the finished component. PhD Position in Machine Learning and Computer Simulation Reference code: 50145735_2 – 2025/WD 1