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expertise and supervision of experienced researchers from multiple institutes at Forschungszentrum Jülich. As one of Europe’s largest and most multidisciplinary research centers, Forschungszentrum Jülich
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Robotisation (PROMAR) group, headed by Matthias Rupp. The group develops fundamental and technological expertise in machine learning for materials science, including data-driven accelerated simulations and
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2025. Encouraged by the continuing success of modern machine learning (ML) techniques, researchers have become ambitious to develop ML solutions for challenging science and engineering problems with
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workstreams, and the PhD’s will be working along senior staff to perform tasks in different workstreams, in strong collaboration with multiple international partners and fellow PhDs from all over the world. Key
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datasets, therefore, there will be a focus in the implementation of models for large volumes of data. The project will work in an exciting interface of statistics and machine learning and has the potential
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of the PhD topic (subproject A7- Reinforcement learning for mode choice decisions): This PhD project will develop and implement a Deep Reinforcement Learning (DRL) model for dynamic mode choice within
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spatial omics datasets. The position will also contribute to multi-modal data integration efforts that combine imaging, genomics, and machine learning approaches. Key Responsibilities Data Processing
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(Wissenschaftszeitvertragsgesetz - WissZeitVG). A shorter contract term is possible by arrangement. The position aims at obtaining further academic qualification. Professional assignment: Chair of Machine Learning for Spatial
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dynamics, data science, and machine learning are beneficial. What we offer: We offer a position with a competitive salary in one of Germany’s most attractive research environments. TUD is one of eleven
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effort at the intersection of machine learning and applied mechanics. The focus of this position is on extracting information about what a neural network has learnt in a symbolic and (human) interpretable