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Materials explores materials for lithium-ion batteries by going beyond conventional graphite anodes and present-day cathode materials. To leverage on our scientific profile exploring digital methods
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into two main thematic strands: »Marginalised Knowledge« and »Digital Participation«. On the one hand, the department’s work focuses on actors from marginalised groups historically and today. Among other
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Postdoc (f/m/d): Machine Learning for Materials Modeling / Completed university studies (PhD) in ...
for Advanced Systems Understanding (CASUS) is a German-Polish research center for data-intensive digital systems research. We invite you to be part of our diverse and international team at CASUS in Görlitz
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for data-efficient exploration and optimization within the process parameter space as well as for adaptive, data-driven machine learning to map the electrolysis process to a digital twin. Data workflows and
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digital tools for biodiversity assessment, thereby contributing to long-term agricultural sustainability and resilience across diverse pedo-climatic zones. To support the activities of the project and
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for tomorrow's industry. Integrated into the Chair of Brewing and Beverage Technology is the working group BioPAT and Digitalization, which deals with issues from the fields of PAT technologies, bioprocess
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scientific career. About us TUM’s new Computational Pathology and Medical Machine Learning lab (*2021) develops methods of machine learning (ML) and artificial intelligence (AI) for the analysis of digital
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09.06.2022, Wissenschaftliches Personal As part of an initiative by the community service foundation Dieter Schwarz Foundation (DSF), TUM created a teaching and research facility
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PhD/Postdoc position in trustworthy data-driven control and networked AI for rehabilitation robotics
11.02.2022, Wissenschaftliches Personal The Chair of Information-oriented Control (ITR) offers a PhD/PostDoc position within the 6G-life Research-Hub „Digital transformation and sovereignty
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application to the European mission of a Digital Twin Earth. ML research directions will include physics-aware machine learning, reasoning, uncertainty estimation, Explainable AI, Sparse Labels and