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and implement computational techniques in processing neuroimaging including CT and MRI modalities. Demonstrated ability in big data analytics and machine learning techniques. Expert ability in
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data Experience with GIS/RS and database environments (e.g., ArcGIS and Quantum GIS) Experience with machine learning and statistical learning Experience working with large, diverse datasets Familiarity
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track-record in first-author scientific publications for Postdoc applications Experience with data-driven machine learning methods for modelling (PINN, Sparse Symbolic Regression methods) High willingness
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jurisdictions utilizing land use-value assessment estimates. Duties include, but are not limited to: development of computational methods, maintenance of current models and data sets, identifying and testing
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Lightweight and flexible solar cells Space deployable structures Device analysis in space environments Big data, AI, and machine learning for space solar initiatives Outstanding Postdoctoral Training Strategy
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of enabling large-scale redox flow battery deployment for energy storage. Scope of Work The successful candidate will work on: Designing and optimizing extraction processes for vanadium from primary and
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will also cover clinical translation focusing on performing editing in large clinical-scale setups. The project is carried in the research group of Professor Jacob Giehm Mikkelsen at Department
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materials to post-mortem gas analysis. This team is completed by data scientists and electronics engineers. A large anhydrous room is available to build Li-ion cell prototypes, as well as to develop any
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want to contribute to tackling one of the big challenges of our time – then you may want to apply to the position in our team, which is expected to start on 01.11.2025. Appointment is conditional
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scientific journals and conferences Your Profile: A doctoral degree in Physics, Materials Science, Computer Science, Data Science, or related fields Proven experience with large language models (LLMs), natural