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strengths of the University of Tübingen in Computer Sciences and Machine Learning. Potential research directions include, but are not limited to, phylogenetic, demographic, ecological and biogeographic
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the INW-1 machine learning team on data handling, online analysis, design of experiments (DoE), and data categorization to enable efficient and automated evaluation of operando experiments Collaboration
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applies), preferably via the TUD SecureMail Portal https://securemail.tu-dresden.de by sending it as a single pdf file to mlcv at tu-dresden.de or to: TU Dresden, Chair of Machine Learning
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Your Job: Machine Learning (ML) and artificial intelligence (AI) based on neural networks are currently reshaping all aspects of society. In several areas, such as medicine, AI-based tools
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present. Evaluation of the trained models on suitable datasets. What you contribute Good knowledge in the field of machine learning and training neural networks. Good Python skills, preferably some
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will also profit from the vibrant research community around machine learning of the SCADS.AI center (https://scads.ai ) and the recently granted Excellence Cluster REC² – Responsible Electronics in
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action recognition, and enable seamless collaboration between humans and machines. Long-Term Human-Technology Evolution: investigate the longitudinal impact of human-technology interaction on learning
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recognition, and enable seamless collaboration between humans and machines. Long-Term Human-Technology Evolution: investigate the longitudinal impact of human-technology interaction on learning, behavior, and
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a soldering station, hardware tools, as well as using a computer You are willing to expand your knowledge to cover the entire range of sample environment at JCNS You are a good team-player when it
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Artificial Intelligence (AI) and Machine Learning (ML). In this position students will contribute to research projects in CKL and as part of their education, will also engage in a dedicated 6-months internship