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Your Job: The main objective of this PhD project is to achieve a better understanding of the efficient propulsion of trypanosomes through complex crowded environments, mimicking biological tissues
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complexity, which prevents approaches that solely rely on classical mechanistic modeling or classical machine learning. Equal opportunity is an important part of our personnel policy. We would therefore
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architectures and principles from Bayesian neural networks and biological sequence models, including large DNA and protein language models. The project also aims to develop a prototype federated learning
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investigate and develop innovative memory solutions in advanced CMOS technologies such as FDSOI. The development of integrated circuits also plays an important role in making these networked devices and their
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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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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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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
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complex, involving multiple senders and receivers interacting simultaneously within a dynamic network. Social groups also exhibit preferred and avoided associations, creating heterogeneous social structures
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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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to test complex mixtures and identify their risk drivers. The position is allocated to the junior research group and supports research on regulatory concepts for mixture risk assessment. As a long-term goal