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generation of MOF water adsorbents with optimal indoor air humidity control performance by leveraging state-of- the-art high-throughput (HT) computational screening based on Machine-Learning Interatomic
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during a doctoral dissertation completed within the laboratory and the INNO-REV[1] project. Its objective is to develop and validate innovative passive techniques for improving the performance of latent
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, CeRh2Si2, TbB4 etc.) near metamagnetic transitions and unconventional superconducting phases. Within step 1, magnetization probes will be developed, tested and compared before choosing the most optimal
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experimental trials on metal additive manufacturing processes - Ability to design and execute experimental plans (DoE) for process investigation and optimization - Ability to acquire, process, and analyze
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numerical techniques to study collective states in active matter. Depending on the candidate's profile and interests, the research can involve various aspects of the modeling and the optimal control of active
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to the lab's research aimed at understanding how polyploid cells regulate their optimal size while maintaining their function. This effort is part of the lab's broader project to study ploidy transitions
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from annotated subglacial bedforms; (iii) designing, training, optimizing and assessing deep-learning detection/segmentation models; (iv) producing a database of outlines and morphometric parameters
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over a very large area, using sputtering deposition techniques. The overall objective of the project is to define the parameters of this technology (integration of high-performance phase-change materials
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. The activity of the IMAGES team of the IDS department in LTCI covers many aspects of the processing, analysis and synthesis of digital images, volumes, and videos. A particularity of the team’s work is
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to several aspects of the project, including: -design, development and validation of experimental optical microscopy setups -modelling and optimization of experimental parameters -adaptation and further