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advanced retrieval techniques, including spatio-temporal regularization, and hybrid methods with machine learning and deep learning. He/she will support the development of an improved forest RTM that can
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and uncertainty mapping at satellite, airborne and drone levels. You will explore advanced retrieval techniques, including spatio-temporal regularization, and hybrid methods with machine learning and
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SD-26083 – POSTDOCTORAL RESEARCHER IN THE CHEMICAL VAPOR DEPOSITION OF METAL ORGANIC FRAMEWORKS F...
chemicals and fuels. The postdoctoral researcher will work on the chemical vapor deposition and engineering of metal organic frameworks for energy applications (WP4). Comprehensive characterization methods
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SD-26084 – POSTDOCTORAL RESEARCHER IN THE CHEMICAL VAPOR DEPOSITION OF COVALENT ORGANIC FRAMEWORK...
chemicals and fuels. The postdoctoral researcher will work on the chemical vapor deposition and engineering of covalent organic frameworks for energy applications (WP3). Comprehensive characterization methods
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and software tools. · Carry out experimental validation of simulation results Is Your profile described below? Are you our future colleague? Apply now! Education · PhD in computer science
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, the CSATLab , our SW Simulators , and our Facilities . For further information, you may refer to https://www.uni.lu/snt-en/research-groups/sigcom/ . Your role Develop innovative methods and data-driven AI tools
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contact Your profile PhD in Inclusive education, with a focus on at least one of the below areas: Inclusive pedagogy (critical perspectives on UDL) Participatory research Research ethics Comparative
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scientifically contributing to projects in Robotic Manipulation for Space Robotics. The candidate will carry a leading role in this area and support PhD candidates in their thesis research. The candidate will work
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budget and time constraints, facilitating flexible adoption of the methods. The project is carried out in close collaboration with Helical-AI, an industrial partner specialized in large-scale genomic
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closely related field PhD training covering topics such as computational modelling, numerical methods, statistical analysis, machine learning or data-driven analysis of complex systems Experience 0–3 years