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learning and data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms
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, Heidelberg and Mannheim, our researchers harness interdisciplinary collaboration to decipher the complexities of disease at the systems level – from molecules and cells to organs and the entire organism
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and materials research that could not be addressed so far due to their high complexity, which prevents approaches that solely rely on classical mechanistic modeling or classical machine learning. Equal
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1 November 2025 to 31 October 2028. Who we are: The Independent Research Group Receptor Biochemistry harnesses the complex interplay between proteases and receptors during plant-pathogen interactions
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is funded from 1 November 2025 to 31 October 2028. Who we are: The Independent Research Group Receptor Biochemistry harnesses the complex interplay between proteases and receptors during plant-pathogen
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international network structure in order to integrate existing competences and knowledge, and to link various actors within the complex area of climate change. The PhD position is also supervised by Prof. Dr
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Degree PhD Systemic Neurosciences Course location München In cooperation with Find out about our network of collaborating research institutions at https://www.gsn.uni-muenchen.de/research/network
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student (m/f/d) to conduct research on a DFG-funded project focused on trace fossils, and the evolution of behavioral complexity over the Ediacaran-Cambrian boundary. This project will combine detailed
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programming and know how to use version control. ▪ You are experienced in the usage of machine learning (e.g., Actor-critic algorithms, deep neural networks, support vector machines, unsupervised learning
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networks involved in CHC perception, particularly in the context of prezygotic reproductive isolation within a species complex of parasitoid wasps (Nasonia). Our previous research has already deciphered