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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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Project (PhD Position) – Accelerated Materials Development by Combining High-Throughput and AI Approaches Your Job: The accelerated development of advanced materials is essential for addressing major
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Your Job: The PhD candidate will be in charge of: The development of photopolymerizable hydrogel blends with n-type and p-type semiconductor capabilities The pattering of the semiconducting hydrogel
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encouraged (e.g. South 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
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an interdisciplinary environment, very good communication skills in English; willingness to acquire German language skills is expected and will be encouraged Desirable skills PhD in biodiversity informatics
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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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Biological sciences » Biology Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Germany Application Deadline 1 Nov 2025 - 23:55 (Europe/Berlin) Type of Contract Temporary Job
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knowledge in physical oceanography, climate change, Earth Observation, Big Data and data science as well as machine learning. This is an exciting opportunity to work on an exciting and ambitious project with
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Your Job: The PhD project is methodologically independent, with the opportunity to contribute to collaborative efforts at the interface of data science, imaging, and materials research. You will
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremen, Bremen | Germany | 28 days ago
microbial communities from deep-sea mining test sites and polar oceans under climate change. The goal of the proposed PhD work is to design a new form of network-enabled explainable ML, as well as trained ML