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to microclimatic variations induced by local vegetation. The researcher will contribute to the analysis of vegetation–climate–technology interactions to optimize energy yield in environments subject to climatic
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Job Description As part of our laboratory's research initiatives, we are conducting advanced research on the computational modeling and optimization of heterogeneous catalysts for various catalytic
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research team. The successful candidate will work on optimizing methods for nanofibers extraction from various types of biomass, focusing on the development of efficient, sustainable, and scalable processes
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digital twins to develop innovative solutions for monitoring, analyzing, and optimizing urban systems in real time. The candidate will contribute to modeling interactions between physical and digital
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of water intended for agricultural use. The selected candidate will contribute to the development and optimization of desalination processes (membrane-RO), the analysis of treated water quality (especially
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the estimation of some of the significant water balance components. The candidate is expected to seek an optimal integration between the physical representations of the various processes and the computing power
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are gaining significant interest for sustainable seawater desalination. This postdoctoral position is part of a research project aiming to design, optimize, and evaluate electrochemical systems [electrodialysis
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, the validation is monitored through design of experiments and optimization of workflows much more rapidly than conventional approaches. The HTMR is a 100% Applied Sciences research laboratory, and is setting up
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. The successful candidate will collaborate with cross-disciplinary teams (biologists, engineers, chemists) to design, optimize, and operate bioreactors, digesters, and other equipment. By employing advanced
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valorization of microalgae for biotechnological applications. Successful candidate is expected to have: Experience in isolation, culture techniques of microalgae and cyanobacteria strains, growth optimization