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Post-Doctoral Position - Medical Image Processing - (240000Y9) Post-Doctoral Fellow Position in Medical Image Processing (Deep Learning for Trauma CT) The Trauma Radiology AI Lab (TRAIL) in
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inference methods (e.g., fixed effects, IV, DiD) Advanced regression techniques - Strong programming and analytical skills (R, Stata, Python, Mplus, or similar). - Evidence of research productivity or strong
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areas Biomedical applications, social determinants of health or other demographic health areas Spatial microsimulation, spatially weighted regression, combinatorial optimization or Bayesian network
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, the emphasis of the post is on quantitative elements. The postholder should begin the post with at least intermediate statistical skills (e.g. conducting well thought out regressions). The postholder should also
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such as classifier free guided diffusion models, transformers with multi-headed attention, physics-informed neural networks, materials foundational models with multi-task learning, symbolic regression
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with DFT or electronic-structure codes such as VASP, Quantum ESPRESSO, CP2K, ABINIT, GPAW, Gaussian, ORCA, Q-Chem, or related packages. Strong materials science or chemistry domain knowledge, such as
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Post-doctoral Fellow/Senior Research Assistant in the Centre for Information Technology in Education
linear regression, mediation analysis, multilevel modeling, and/or latent variable models. Experience in managing and analyzing large datasets and in using generative AI tools for research purposes is a
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, imaging). • Solid foundations in signal processing and statistics. • Experience with machine learning for regression (e.g., tree-based methods, neural networks) • Hands-on experimental skills: ability and
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19.07.2022, Academic staff The Machine Learning and Information Processing group at TUM works in the intersection of machine learning and signal/information processing with a current focus on deep
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the population at large, and multinomial regression analysis to assess their social bases. Building on the results from the previous phases, we will develop a tailor-made representative survey experiment to assess