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Organisation Job description Within the Department of Operations, a post-doc position is available on the topic of logistics for customization in education with self-regulation. A paradigm shift is
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. Our work supports the health assessment of marine mammal populations and informs conservation efforts for the North Sea ecosystem. Your research will cover key topics such as disease, reproductive
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Postdoctoral Researcher in Citizen Science & Societal Engagement for an Inclusive Energy Transition,
models for upscaling of the heat transition", and you will be primarily involved in the Work Package that focuses on "Effective and inclusive citizen engagement and support for scalable PED designs
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: 12 September 2025 Apply now Are you a data scientist interested in designing and implementing process-informed machine learning and uncertainties quantification methods? Join us as a postdoc and work
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-derived organoid models. You will work closely with in-house technology platforms, including the Single Cell Genomics Facility, Big Data Core and High Throughput Screening Facility. Our research is embedded
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characterization of 3D structures using microscopic and tomographic techniques. You will also work with architects, designers, and engineers to develop biomimetic models of fiber-reinforced structures in order to
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: 12 September 2025 Apply now Join the faculty of Geosciences as a postdoctoral researcher! You will work on carbon turnover models and the fusion of data science forecasting methods with process-based
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, and other techniques. The project will hire a postdoctoral researcher, who will work in the GOLEM lab. Your tasks: In collaboration with the other team members, you will work on the creation of a
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industrial applications of analytical systems with an end user-focused mindset is preferred; Curious and creative in providing tailored solutions; Good team spirit and able to work independently; Good
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partners to reduce CO2 emissions in steel production using machine learning. You can find more information here . You will work on a theoretical and an applied project on data-enhanced physical reduced order