193 phd-in-computational-mechanics-"St"-"FEMTO-ST" positions at Technical University of Munich
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Robotics, Mechanical Engineering, Electrical Engineering, or a closely related field Proven research experience and publication track record in robotic manipulation, deformable object handling, or related
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for the School’s PhD program. We offer regular public lectures and symposia, weekly discussion groups, and visiting researcher programs. We maintain close collaborative ties to other parts of TUM as
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16.08.2023, Wissenschaftliches Personal The Chair of Computational Modeling and Simulation (CMS) at the Technical University of Munich invites applications for the position of a Research Assistant
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for deformation modeling and prediction Integration of perception, planning, and control for robust real-time robotic performance Requirements Ph.D. in Robotics, Mechanical Engineering, Electrical Engineering, or a
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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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networks self-organize their architecture. We are looking for a PhD student (m/f) to join our team at the TUM. Task Flow networks are a fundamental building block of life. Transport by flow is the main task
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/d) in Energy Informatics. You are passionate about applying cutting-edge information technology to solve the energy and climate crisis and would like to work in a vibrant and international research
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in Life Sciences or in Computational Biology • Experience in flow cytometry, cell culture and in high-dimensional single-cell data analysis and programming skills are a plus • Organizational skills and
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. The main focus is developing and characterizing metallic high-performance materials for/through additive technologies using experiments and computer-aided methods. Furthermore, the chair is dedicated
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, enrichment analyses - biological interpretation of data Your qualification - PhD/MSc degree in bioinformatics, computer science, mathematics, life sciences - background in Machine Learning and/or RNAseq