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, Wissenschaftliches Personal The Livestock Systems research group at the TUM School of Life Sciences is recruiting a postdoctoral researcher (m/f/d) to work on grassland restoration. The aim of our group is to improve
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, or machine learning is also appreciated. PhD: The candidate is expected to have some background in theoretical computer science, including some of the following areas: automata, logic, games, verification
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observations, focusing on indoor environments. Qualifications: • Applicants for a PhD must hold a Master’s degree in computer science or equivalent • Fluent written and spoken English skills • Proficient C
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engineering, computer science or electrical engineering, with good grades. Experience in scientific work, project proposal writing and team leadership are part of your repertoire. In addition, you are
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, computer science, mathematics, physics, or a related field with an outstanding academic record. Interest in mathematical signal processing, optimization, and/or machine learning is important. Since
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) and present your work at top conferences and journals in our field. Candidates should have completed their Master/Diploma studies in Computer Science, Mathematics, Mechatronics, Electrical Engineering
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the following areas: PhD in mechanical/electrical engineering, robotics, computer science, or a comparable field, Experience in self-reliant managing of research projects (financial and administrative) and
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control and strong interest to further develop their skills. The selected applicants are expected to have: • Masters or PhD-level degree in Robotics, Mechatronics, Computer Science or closely related fields
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coordination with Prof. Dr. Harald Sack. Qualifications and Skills: A very good PhD degree in computer science or a comparable discipline. Very good software engineering skills. Excellent written and spoken
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management, (organizational) psychology, sociology, economics, business informatics, or related subjects, • with a strong passion for entrepreneurship and/or family business research, • with a solid knowledge