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Field
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-omic data integration. Computational skills for data analysis (e.g., Python/R, workflow tools, and reproducible research practices). Prior work related to neurodevelopmental disorders, regulatory
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software (R, python). ● Experience in microbiological and molecular techniques such as sterile technique, DNA extraction, PCR, synthetic community design, etc. ● Experienced in designing randomized plant
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Bayesian inference, likelihood-free inference, uncertainty quantification, identifiability analysis, or scientific machine learning. Strong programming skills (Python, Julia, Matlab, C++, or similar). Strong
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Strong programming expertise in Python and experience with frameworks such as PyTorch, Hugging Face Transformers, or equivalent tools Demonstrated ability to develop scalable, automated, and reproducible
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spintronics, or cavity spintronics. Proficiency in scientific software development (e.g., Python, COMSOL, HFSS). Demonstrated ability to work independently and collaboratively in a multidisciplinary environment
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, co-expression analysis, de-novo assembly, read alignments, whole genome polymorphism calling, and population genomics. Must be proficient in R, BASH, Python, Java, and/or Perl programming languages
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experimental physics, nanofabrication, low temperature measurements, THz optics, Python programming would be an asset. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UPR2940-ELOBER-173
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control and related). Experience with experimental methods and data acquisition systems like LabView or similar systems will be beneficial Strong, proven programming skills in Python. Proficient in written
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quantitative discipline. You will have experience analysing complex, high-dimensional datasets and applying quantitative or computational methods, together with strong programming skills in Python or a
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programming ability in Python, and practical experience with a modern deep learning framework such as PyTorch. Experience working with 3D data in some form: volumetric images, meshes, point clouds, or geometric