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research, developing novel computational models to analyze histopathological and multi-omic data. Opportunity to build a collaborative scientific career in computer science and medical data analysis
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-)Statistics, (Bio-)Informatics, Computer Science or related disciplines Strong background in modeling multi-modal data (images, tables, text, etc) Understanding of biases and causal inference Experience with
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22.04.2022, Wissenschaftliches Personal The Professorship for Environmental Sensing and Modeling at the Faculty of Electrical Engineering and Information Technology is researching topics
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Institute (https://www.mdsi.tum.de/). The Position Plan, develop and test novel computational models for the analysis of digital pathology image data. Collaborate with pathologists and other domain experts
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scripting language is necessary for prototyping. Interest and affinity for high-performance computing are necessary for the position. You should have experience with the roofline model and familiarity with a
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management. Our group combines empirical work (with experiments in the field and in the lab) and modelling techniques. The focus of this postdoctoral position is the generation of empirical datasets
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focus on deep networks for solving inverse problems, learning robust models from few and noisy samples, and DNA data storage. The position is in the area of machine learning, with a focus on deep learning
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(with experiments in the field and in the lab) and modelling techniques. The focus of this postdoctoral position is the generation of empirical datasets for livestock systems in East Africa, and in
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communication system are modeled using information theory. We wish to investigate how interleaving can reduce the overhead and computational load due to coding coefficients required in classical linear random
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materials science • Extensive knowledge of computer-based modelling and simulation methods in materials science of metals, e. g. Calphad method, precipitation simulation, cellular automata, kinetic Monte