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science or related fields Solid knowledge in semiconductor materials, epitaxy, and thin film characterization (experience in any of these areas is a plus) Interest in quantum information science and quantum
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-performance computing and imaging facilities. a collegial, international atmosphere and flexible, family-friendly working hours a structured PhD programme with extensive training and career-development
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functionalities. This highly collaborative project, jointly investigated by PDI, TU Munich, University of Münster and HTW-Berlin, is funded by DFG within the priority programme SPP2477 "Nitrides4Future
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. This highly collaborative project, jointly investigated by PDI, TU Munich, University of Münster and HTW-Berlin, is funded by DFG within the priority programme SPP2477 "Nitrides4Future". The motivation is to
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the Research Group “Stochastic Algorithms and Nonparametric Statistics” in the project „SFB/Transregio 388“ SFB/Transregio 388 investigates the interplay between rough analysis and stochastic dynamics. Central
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Computer-adaptive methods and multi-stage testing Application of machine learning in psychometrics Predictive modeling of educational data Methodological challenges in cohort comparisons Advanced meta
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., KonsortSWD, NFDI), including working groups and collaborative initiatives. Your profile Completed PhD (or near completion) in a relevant field such as economics, statistics, data science, informatics, or
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year Interest in an academic career and in topics related to resilience and resilience promotion, as well as in the analysis of neuroimaging data and/or the computational modeling of psychological
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environment. The candidate will be given the opportunity to develop a doctoral thesis with extensive support through the RTG2413 (www.synage.de ) and the CBBS graduate program (gp.cbbs.eu ). Your profile
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the era of large population size and dense genomic data such as whole-genome sequencing, new algorithms are needed to remove the bottleneck of computational load for such a development. In the frame of a