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Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg | Magdeburg, Sachsen Anhalt | Germany | 3 months ago
used in agriculture and industry, is not only a compelling candidate for storing and distributing H2, but also a potential fuel and a valuable chemical. On the aforementioned ground, recycling oxidized N
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pair distribution function (PDF) data for structure refinement and modelling of heterogeneous catalysts Publication of results in peer-reviewed scientific journals and presentation at conferences Your
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Infrastructures Didactics of Informatics Digital Humanities Distributed Systems High-Performance Storage Machine Learning Medical Informatics Neural Data Science Practical Informatics Scientific Information
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promising lean alloy system for additive manufacturing, as the mechanical properties can be tailored through phase composition, distribution and morphology by tuning process parameters. The work is carried
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and Master’s students in Informatics and Data Science. Supervise Bachelor’s and Master’s theses. We Offer Practice-oriented research projects with leading academic and industry partners (like Google
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Degree Dr Course location Darmstadt Teaching language English Languages The programme is in English only. Participants have to write their doctoral theses in English. Full-time / part-time full-time
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Course location Gießen Description/content A Structured PhD Programme Within a Cutting-Edge Research Environment At the GCSC, you will actively participate in a research-intensive environment from
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insights into the dynamic distribution patterns of human tissue resident T helper cells across space and time. Topic: Dissecting the body-wide spatio-temporal organisation of human resident T helper cells T
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privacy-preserved fashion. Research topics include, but not limited to, i) handling distributed DL models with data heterogeneity including non i.i.d, and domain shifts, ii) developing explainability and
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privacy-preserved fashion. Research topics include, but not limited to, i) handling distributed DL models with data heterogeneity including non i.i.d, and domain shifts, ii) developing explainability and