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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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pipelines and interfaces): Python (numpy, scipy, pandas, PyQt5, matplotlib, pympi.Elan, torch, etc.), R/RStudio, automatic speech processing systems (e.g., Nemo, MFA), and NLP packages (such as spaCy
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design, data analysis, and programming (e.g., SPM, MATLAB, Python, R, etc.) Strong scientific writing skills Excellent organizational and communication skills Willingness to work in an interdisciplinary
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particular simulation-based inference), strong programming skills in Python and experience with HPC environments. Experience with galaxy redshift surveys and/or gamma-ray astrophysics is desirable. We welcome
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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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Python and/or SPICE - Ability to transmit knowledge (written/oral) and provide supervision - Sense of confidentiality - Excellent interpersonal skills, autonomy, strong adaptability, and a team player
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profile with advanced skills in: - Numerical programming in Fortran, C, or C++, and Python, - Spectral methods using Fast Fourier Transform (FFT), - Code debugging. He should have a keen interest in
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proficiency in Python. Knowledge in Statistical physics and Network science, and interest in complex networks and interdisciplinary research are a plus. The position is for 3 years, and will be located
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will use and develop Python scripts for analysing results and may participate in the development of codes such as the observation simulator and the improvement of the controller. The proposed thesis will
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datasets; • Strong analytical and statistical skills (preferably in R or Python); ability to analyse spatial data (with GDAL/PDAL via R or Python) ; • Solid background in fire ecology, ecosystem functioning