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Postdoctoral researcher (M/F). Modeling damage during earthquakes. Comparison with geophysical data.
. Use of digital earthquake models; The code used for calculations is written in Fortran, and the tools for processing input and output data are written in MATLAB and Python. The postdoctoral researcher's
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). Conduct in vivo optical imaging experiments on healthy volunteers and patients; Develop image-analysis pipelines (Python, MATLAB) and model functional optical signals; Optimize and maintain the AO-RSO
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transcriptomics data and network-theoretic approaches. - design of a new mathematical method - monitoring and study of publications relevant to the field - programming/coding in Python (Pytorch) - presentation
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, library preparation, cell culture, and imaging - Proficiency in computer languages (bash, python, awk, R) - NGS/omics data analysis - Proficiency in statistics for high-throughput data analysis - Generation
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of statistical physics. Technical Skills: Proficiency in data analysis and modeling. Programming: Mastery of at least one programming language (Python, C++, Fortran, etc.). Experience: A minimum of 4 years of post
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proficiency: Matlab, Python, R, SPM, CONN; Very strong knowledge of neurophonetics, particularly stuttering and verbal disfluencies; Solid background in neuroscience and in the neuropsychology of language and
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the performance and sensitivity of a future space mission for cosmology. The candidate will be required to simulate the performance of a specific instrument using Python code in order to predict the sensitivity
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in nanomagnetism and spintronics with emphasis on magnetic skyrmions - Expertise in electrical and MOKE measurements - Expertise in micromagnetic simulations - Experience with Python - Micro/nano
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Team : We seek a researcher with demonstrated experience in perturbative modeling of LSS (in particular biased tracers of dark matter), analysis of simulated datasets, and strong programming in Python
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conferences. • Contribute to the writing of scientific publications. Optional : • Design Machine Learning (ML) potentials. • Code in FORTRAN and PYTHON to improve the functionality of the global