52 machine-learning-modeling-"Linnaeus-University" PhD positions at Technical University of Munich
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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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research and travel budget available to best support your research. You will partic-ipate in teaching and supervising students, interact with and learn from the other team members, and re-ceive close
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learning of autonomous vehicle systems. The AVS Lab's research is motivated by the goal of developing the next generation of intelligent autonomous vehicle systems. We are seeking highly motivated and
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position is part of a large cooperation project with partners from industry and academia. The candidate will measure and model data on the reaction kinetics and mass transfer in reactive chemical systems. A
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at start: • Strong background in T cell biology/immunology. • Hands-on experience with transgenic mouse models, including breeding and colony management. • Proficiency in preparing and processing lymphoid
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). The successful candidates will have the opportunity to learn a wide spectrum of genetic, molecular and cell biology, biochemical as well as immunological techniques. They will be exposed to cutting edge methods
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machine learning technologies. This PhD position is part of the project “Artificial Intelligence for the automated creation of multi-scale digital twins of the built world”, which is funded via the Georg
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, C++, etc.) Knowledge of machine learning, data mining, or related fields Excellent communication skills and ability to work in a collaborative team environment Interest in social science research
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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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analysis (TEA) or an affinity towards these research questions. - Basic knowledge in bioprocess design, bioengineering and/or mathematic modeling - Affinity towards research question in life cycle