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from machine learning methods to more traditional statistical and econometric techniques. We are driven by science with purpose, pushing the academic frontier by publishing at the highest level in
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Intelligence) is being expanded into a leading German AI competence center for Big Data and Artificial Intelligence (AI). For TUD Dresden University of Technology, diversity is an essential feature and a quality
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health. Successful candidates may have experience in electron or X-ray microscopy, image analysis, AI and machine learning, quantitative data science or computational modelling. They will be able to work
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Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. | Dortmund, Nordrhein Westfalen | Germany | 3 months ago
fields Experience with image analysis, or computer vision Good knowledge of basic machine learning techniques, such as variational autoencoder Good presentation and writing skills Proactive, independent
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of the project thereby complementing the human data analyses performed in Maastricht. Post-translationally modified proteins (PTMs) and their antibodies have been long-implicated in the etiology of auto-immune
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are applied in real-world settings characterized by large-scale networks, stochastic demand, operational disruptions, and complex constraints. A central research question is how machine learning can be
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cutting-edge computer vision, wearable sensors, and citizen science, RUN2GETHER will capture large-scale, real-world data during running events and group runs. These data will advance our understanding
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analyses or game theoretic analysis. Experience with large language models, machine learning, and/or programming in R or equivalent programs is an advantage but not a requirement. The evaluation of
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the running infrastructures. Using cutting-edge computer vision, wearable sensors, and citizen science, RUN2GETHER will capture large-scale, real-world data during running events and group runs. These data will
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PhD in Computational Simulations of Turbulent Reaction Flows for Clean Energy and Sustainable Propul
machine-learning methods, you will analyze flame-turbulence interactions, pollutant formation, as well as unclosed terms relevant to LES modeling. The analysis involves the fluid dynamic as