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us We are TUM’s unique Pathology AI lab developing new machine learning (ML) methods for automatically analyzing digital pathology data and related medical data. Such methods include the automatic
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of healthcare, science, technology, society, and the environment. Our mission encompasses both theoretical and empirical methods to foster ethical, transparent, and interdisciplinary research, education and
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numerical studies. Develop NanoLPC application in additive manufacturing by developing a multiscale simulation tool for keyhole dynamics and pore formation prediction suitable for PBF applications. Expected
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Academic staff for the "Learning Sciences and Educational Design Technologies" working group (f/m/d)
/transnational community of practices (COPs), co-creating an “agents of change” program, and facilitating cultural literacies across borders through a dynamic pedagogical intervention. Qualifications We seek
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methods for the design, verification, and test of circuits and systems for conventional as well as alternative and post-CMOS computing technologies. Besides that, we have successfully applied the methods
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26.02.2025, Wissenschaftliches Personal We are looking for a postdoctoral researcher (f/m/d) with a PhD in Simulation Technology, Computer Science, Mechanical Engineering, or a related field. About
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, or learning sciences. You are interested in interdisciplinary collaboration and working in research teams. You have very good knowledge of social science research methods and statistics. You have
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research methods and statistics. - Experiences with research syntheses (e.g., meta-analyses) and video data analyses would be an advantage. - You have very good written and spoken Eng-lish skills and good
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to the road? Then this position is just right for you! About us In the Autonomous Vehicle Lab, we develop the vehicle of the future with intelligent algorithms and methods. We are involved in numerous projects
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the possibility of an extension. TASKS: Mathematical modeling and development of inverse methods (e.g. Bayesian inversion, optimization based methods, sparsity promoting methods based on L1-norm minimization and