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to cover a wider variety of physics use cases. Developing methods to make machine-learning-based models portable and interoperable. Leading the definition of containerized and networked “Models as a Service
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-party research funding are expected. We are particularly interested in a candidate in any field of economics who leverages state-of-the-art machine learning and causal inference methods to innovative
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Documented expertise in developing and training machine learning models (ideally with a focus on LLM), high-performance computing, data management, and software architecture Strong Python programming skills
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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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European research consortia such as the DAPHNE (DAta for PHoton and Neutron Experiments) NFDI consortium and the Cluster of Excellence "Machine Learning: New Perspectives for Science". Details
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-scale research facilities (e.g. DESY, ESRF), including coordination and setup of experiments Development of data workflows and analysis strategies (in collaboration with our machine learning team
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funded German research initiative. Project Description: Carbon black is an indispensable component of numerous everyday products – from car tires and seals to paints and plastics. However, its production
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institution of TUM Campus Heilbronn that uses data to answer relevant questions and solve real-world problems. It brings together fundamental, methodologically driven research in optimization, machine learning
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your help! We have several fully-funded open PhD and Post-Doc positions (m/f) A list of concrete potential projects: Development of modern auto-differentiation (JAX-based) physics simulators
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Oldenburg Oldenburg, Niedersachsen | Germany | 17 days ago
and strategies. We recently developed machine learning tools to recover plasmids from metagenomic assemblies and characterized their ecology and evolution in the human gut (https://www.nature.com