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learning and signal processing approaches to classify cap types from raw signal traces. Collaborate closely with experimental researchers to guide experimental design and interpret data. Contribute
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made from magnetoelectric materials, which transduce wireless magnetic powering signals into local electric signals that can be used to stimulate neurons. Our multidisciplinary group works in materials
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reliability of R-Mode, particularly under varying environmental conditions. Key objectives include understanding the physical processes that affect R-Mode signal propagation, quantifying the variability
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balance. Lead data analysis efforts, applying advanced signal processing and statistical techniques to extract meaningful insights from electrophysiological and imaging data. Collaborate with
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with fewer data points and tailored reward functions towards design objectives while generating molecules in 3D. Additional requirements: Doctoral degree (PhD) in computational (medicinal) chemistry
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understanding of the processes determing grasslands productivity, nutrient cycling and those leading to grassland soil degradation. • Experience in conducting experiments (field and laboratory). Experience
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19.07.2022, Wissenschaftliches Personal The Machine Learning and Information Processing group at TUM works in the intersection of machine learning and signal/information processing with a current
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grasslands. What we expect from you • University degree (M.Sc.) and Ph.D. in Agricultural Science, Biology, Ecology, Environmental Science or a related field. • Thorough understanding of the processes
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to control of infection. The focus of the project is on virus-specific CD8 T cells and the local hepatic regulation of their effector functions through environmental and cytokine-induced signals. The AG