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Field
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-funded competence centre for Artificial Intelligence and Machine Learning. It now consists of more than 40 excellent research groups, both in the field of application-oriented Machine Learning and in
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Degree PhD Learning Sciences (alternatively Dr phil, Dr rer nat, Dr med; not recommended for international students) Course location München Teaching language English Languages Courses are held
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and evaluation of innovative data- and machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods
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Researcher / Postdoc for molecular investigations on microbial ecology in deep-sea polymetallic n...
apply machine learning/AI methods for ecological analyses Expedition experience Further Information The AWI is characterized by The AWI is characterized by our scientific success - excellent research
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for many applications and processes, like in self-driving cars or to prevent factory workers from being injured by heavy machines. While AI algorithms may achieve great accuracy in the detection of persons
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, innovative research program particularly in the field of systems immunology applying novel high-resolution technologies, and/or computational analysis methods and artificial intelligence/machine learning is
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of study. You have knowledge of artificial intelligence and its application in the analysis of company data. You have experience with Generative AI technologies (e. g. GPT models, machine learning). You are
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optical communication networks and systems, as well as machine learning, computer vision and compressing digital videos. Become a part of our team and join us on our journey of research and innovation! What
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of embedded machine learning, neuromorphic hardware and deep learning accelerators. Want to get more information? Click here. What you will do Responsible for RTL design (VHDL, Verilog) of digital blocks and
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-driven energy systems research – scalable, transparent, and interoperable. Your tasks in detail: Development of a structured description format for the unambiguous and machine-readable characterization