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. Preferred: background and/or experience in microfluidics and image processing. Strong interpersonal skills, teamwork, scientific curiosity, and ability to synthesize information. Where to apply Website https
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. The successful candidate should demonstrate scientific curiosity, rigour, initiative, and an ability to work in an interdisciplinary environment. Experience in optics, high-speed imaging, image processing
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quantification and multi-fidelity modeling. • Ability to analyze and exploit large simulation datasets. • Ability to work in a collaborative multidisciplinary environment. Desired experience : • Experience in
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- Knowledge of molecular biology techniques (DNA extraction, qPCR) - Rigorous and reliable - Initiative and creativity - Ability to work independently and organise experimental work effectively - Good oral and
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., using Python). - Strong interest in research and modeling approaches. - Ability to work in a team at LSCE, IPSL, and within the AMACLIM project. - Synthesis skills (literature review, analysis of model
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, the ability to analyze the full dataset collected by the experiment will be severely limited. The L2IT is a leader in developing new track reconstruction algorithms using geometric deep learning methods
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environment, infrastructures. All developed methods will be validated using real-world datasets from the Montpellier metropolitan area, by assessing their ability to enrich existing GIS databases, reduce
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data from the Simons Observatory to extract the kinetic Sunyaev-Zeldovich contribution from the CMB power spectrum This contribution will then be modelled as two distinct components: the local kSZ signal
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collaborate with the G2Elab modeling team on numerical models of quench dynamics. This development will aim, in the long term, to enhance our ability to predict quench behavior, as well as to serve as a tool
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experimental work; • Strong interest in modeling/simulation, particularly multiphysics modeling, and data analysis; • Autonomy, scientific curiosity, and the ability to work in a multidisciplinary environment