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. 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 for livestock
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Doktorand:innen: Abgeschlossenes Masterstudium in Informatik oder Mathematik. Für Postdocs: Abgeschlossene Promotion (Ph.D.) in Informatik oder Mathematik, idealerweise mit Hintergrund in einem oder mehreren der
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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 the subtropics in
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Postdoc WissenschaftlerInnen aktiv am wissenschaftlich-akademischen Diskurs zur aktuellen Forschungs- und Entwicklungsfragen im Bereich des Grünen Wasserstoffs teilnehmen. Von Ihnen wird eine rege
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learning, safety-critical control, probabilistic modeling, verification, or optimization; Excellent communication skills in English and the ability to work both independently and collaboratively in
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students pursuing an experimental doctoral thesis in basic research in biomedicine Postdocs who are pursuing an experimental project in basic research in biomedicine Graduates who have applied
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-XRF, Raman, FTIR in reflection mode) to enable multimodal data fusion and automated material characterization. • Apply and further develop machine-learning and statistical models (e.g. PCA, SAM
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verified with applications from geodynamics. For more information consider the job description here . Tasks Tasks in the project include the efficient implementation of new models, methods, and algorithms
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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 for inverse
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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 network coding