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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
students with strong theoretical foundations and a desire to contribute to fundamental algorithmic research. Our group works at the intersection of algorithms, machine learning, and interactive visual
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subjects, high interdisciplinary desire to learn, and willingness to cooperate, openness for internationalization and diversity, very good verbal and written English communication skills (good command
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knowledge in food chemistry and superior interest in food systems biology, food-related research • Keen interest in learning and applying experimental biophysical techniques, in particular AFM (essential
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with chemoreception and sensory biological techniques (SSR, GC-EAD, EAG). •Experience in analytical chemistry (GC-FID, GC-MS). •Experience in or willingness to learn statistical data analyses, data
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, Machine Learning, Hyperspectral Cameras • Professional proficiency in written and spoken English Application process Send your application in English by email to amx@wzw.tum.de with the title “Research
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) or quantitative (e.g., surveys, statistical analysis) methods and demonstrate a willingness to learn about the other approach or mixed-methods research. Knowledge of social science approaches (e.g., psychological
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, which will supplement the research training with outstanding opportunities for career development, continued education, and life-long learning. We offer excellent working conditions in a young and
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, Management Science, Business Analytics, or a related field. Strong analytical skills with experience in AI, machine learning, or data analytics. Very good English skills in writing and communication
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models. Your tasks: Research, development, and evaluation of Machine Learning and Deep Learning methods Prototype development Literature review Publication and presentation of scientific results in
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the 01.10.2022. Your Responsibilities: You will work at the cutting edge of privacy-preserving deep learning research with a focus on one or more of the following topics: - Optimal model design for differentially