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Field
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Degree PhD in Experimental Medicine Possibility to receive the double academic degree of MD/PhD Course location Tübingen Teaching language English Languages Courses are held in English (100%). Full
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university degree (M.Sc. or diploma) in the fields of chemistry, biology, renewable resources, material sciences or other thematically relevant subjects •In-depth knowledge of wood chemistry, flavor substances
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Engineering or a related discipline. Experiences in Machine Learning, Deep Learning and Artificial Intelligence. Strong programming skills (Python) Good knowledge of AI frameworks like TensorFlow, PyTorch
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Master’s degree in biotechnology, bioengineering, bioeconomy cell biology, molecular biology, or a comparable field of study, preferably with a background in life cycle assessment (LCA) and techno-economic
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, metal physics, or a similar degree Experience in software engineering (incl. high-throughput computing) Knowledge in the field of materials engineering of metals and materials modelling (i.e. CALPHAD
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mathematics, (theoretical) computer science, machine learning foundations, electrical engineering, information theory, cryptography, statistics or a related field. - Advanced knowledge of probability theory
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12.10.2021, Wissenschaftliches Personal The TUM Professorship for Data Science in Earth Obervation is seeking a full-time PhD candidate on the topic of “Multi-scale Semantic Understanding
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full-time PhD candidate on the topic of “Automatic Recognition of building attributes” About us The TUM-Professorship for Data Science in Earth Observation develops innovative methods for information
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of employment). The successful candidate should possess evidenced (e.g. by relevant PhD thesis and publications) knowledge of stochastic and rough analysis. Knowledge of rough path signatures, numerical methods
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well as eastern and south-eastern European (EU) countries. Eligibility criteria include an excellent academic record and voluntary social and political involvement. PhD students receive material and non-material