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of such high-performance fiber laser platforms requires advances in the development of high-reliability fiber components and optimized doped fibers. The first objective of this PhD project is to explore
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resources to carry out the PhD project. This PhD is part of an interdisciplinary research project, involving mathematicians, molecular biologists, parasitologists, and philosophers from France, the
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PhD student (M/F) in Polymer Chemistry: Synthesis of cross-linkable, degradable and recyclable latex
of degradable polymers and the re-emulsification of degraded fragments to repolymerize them into an emulsion has never been carried out. This thesis is part of the collaborative research project DYNAMIC-LATEX
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electrospinning devices, as well as structural characterization tools (microscopy, X-ray, spectroscopy), and mechanical and rheological equipment. Context This PhD is part of the ANR FLIGHT project, which aims
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equipment, particularly in imaging and electron microscopy, image analysis, and bioinformatics. The team is composed of five people. The thesis will be carried out as part of a collaborative ANR project
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-1 replication, long-read nanopore sequencing, and sample multiplexing during direct RNA sequencing (dRNA-seq). In the first part of the project, the doctoral student will study N6-methyladenosine (m6A
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$ signatures that are expected to particularly benefit from the new trigger system. The second part of the thesis subject concerns physics sensitivity studies to place detector requirements for experiments
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laboratory is studying the behavior of hydrogen in the first wall materials of the International Thermonuclear Experimental Reactor (ITER). This subject is part of a strongly structured European framework
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Description This doctoral contract is part of an interdisciplinary research environment within the Priority Research Program and Equipment (PEPR) "Risks" – IRiMa. Co-led by BRGM, CNRS, and Université Grenoble
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element of embedded intelligence lies in the sensor's ability to self-calibrate and, in particular, to adapt its responses and models according to sensor aging and the (sometimes significant) variability