70 assistant-professor-computer-science PhD positions at Technical University of Munich in Germany
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                • Scientific publishing Your qualifications: • Completed academic university degree (university diploma / M.Sc.) in Computer Science, Geoscience, Physics, Data Science, or comparable subjects • Experience in 
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                and Master’s students in Informatics and Data Science. Supervise Bachelor’s and Master’s theses. We Offer Practice-oriented research projects with leading academic and industry partners (like Google 
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                your research at conferences, workshops, and through scientific publications. Your profile: ▪ You hold an MSc degree in physics, engineering, or computer science. ▪ You are very experienced in Python 
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                Engineering, Operations Research, Civil Engineering, Computer Science, Data Science or a related field, from a university/department with a strong international research reputation Strong mathematical and 
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                the courses Advanced Mathematics 1–2 and/or Statistics at the TUM Campus Straubing. Your profile: Above average master’s degree in mathematics or (theoretical) computer science with a focus on discrete 
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                for dynamical systems, causality, and ML for science more broadly. We publish at core ML venues and are also interested in collaborating on applications in biomedicine, climate, or physics. Find out more at 
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                profile: • Very good degree (Master or Diploma) in aerospace engineering, mechanical engineering, computer science or a comparable field. • Experience in machine elements, structural analysis, fault 
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                23.07.2025, Wissenschaftliches Personal The Ecosystem Dynamics and Forest Management Group at the TUM School of Life Sciences, Technical University of Munich studies how forests change in time and 
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                19.09.2025, Wissenschaftliches Personal The Leibniz Institute for Food Systems Biology at the Technical University of Munich (LeibnizLSB@TUM) is a prominent member of the Leibniz Association and the 
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                to challenging questions in the field of computational material design, especially with the help of CALPHAD-based methods. For further development of our simulation environment (https://github.com/cmatdesign