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thermodynamic cycles by combining two complementary approaches: - Generative models derived from artificial intelligence, capable of proposing new process architectures; - Superstructure-based optimization
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(photomixing). The candidate will work primarily in the IEMN cleanroom, developing and optimizing micro/nanofabrication process flows for laser device realization. A first research axis will focus
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), which is sliced, stained, and ultimately examined by a specialist to diagnose its cancerous nature. This process is time-consuming (1–24 hours) and hinders optimal patient management during surgery or
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the design of optimized electrodes for more efficient and sustainable hydrogen production. The project is interdisciplinary, combining chemistry, physics, and materials science. Where to apply Website https
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) which is sliced and stained to be finally observed by a specialist allowing him to make a diagnosis on its cancerous nature. This process is long (1h-72h) and prevents the optimal management of patients
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, glycogen and future gonads of males and females. The results will allow for a better understanding of the reproductive processes in this insect within the fundamental context of sexual selection. It will
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implement and train neural network architectures, including Physics-Informed Neural Networks (PINNs), in order to integrate physical constraints into the learning process and improve the identification and
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) nanosheets for low-pressure hydrogen storage applications. This position involves close collaboration with academic and industrial partners to design innovative nanomaterials and optimize their performance
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(UV-Vis, IR, Raman…), microscopies (TEM, SEM), electrochemistry, and other relevant physico-chemical techniques. Chemical process development: optimization of reaction conditions, analysis of yield and
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) focuses its research on Solid State Chemistry, Materials Science and Chemistry and Process: designing, preparing, shaping and characterizing materials in order to discover, control and optimize specific