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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The CMS group at IPHC is actively involved in the construction of the CMS Tracker
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) or electrodes in lithium-ion batteries samples as the project advances. The results of the ptychography will be correlated with both chemical as well as structural three dimensional analysis of the same sample
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at the MMSB laboratory focuses primarily on microbiology and structural biology. Within MMSB, the MOMS team is primarily interested in the structure and function of biological membranes, using bioinformatics
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of new electronic and structural properties, which will be explored in the context of neuromorphic applications. The successful candidate will be responsible for: • Fabricating heterostructures by pulsed
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materials based on π‑conjugated small molecules and their solids. The work focuses on establishing robust structure–property–performance relationships that guide molecular and materials design. The researcher
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be primarily conducted within the Structural Metallurgy Group at IRCP, Chimie-Paristech, Université PSL, under the supervision of Dr. Fan Sun, co-PI of the project. The researcher will work in close
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comprises about 170 members and is structured into 4 research teams : Analysis, Numerical Analysis, and Scientific Computing (ACSIOM), Didactics and Epistemology of Mathematics (DEMA), Probability and
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-researchers, 50 engineers, technicians, and administrative staff, and 100 non-permanent staff, including 60 doctoral students. CEREGE is structured into four teams and several analytical and technical platforms
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ethology, chemistry and subatomic physics) develop very high level programs based on scientific instrumentation. The IPHC is structured into 4 departments and has a total staff of 393 staff including 257
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. The objective is to extract structural and metabolic biomarkers enabling precise spatio-temporal modeling of tumor evolution, for diagnosis, prognosis and personalized therapeutic follow-up. Develop deep learning