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development, including firmware programming (C/C++) and interfacing with ADCs, DACs; 4. Good knowledge of programming in Python for data analysis, laboratory automation, and control systems; 5. Strong
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partners. The postdoctoral researcher will also contribute to teaching in areas such as Machine Learning, NLP, AI for Education, Explainable AI, and Python-based applied seminars, supporting course
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neural networks (CNNs) for extracting features from full-field atmospheric data Demonstrated experience coding in Python Experience modeling unsteady wind loads from atmospheric flows Experience in flight
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, Computational Physics, Bioinformatics, Artificial Intelligence or similar. Proven knowledge of scientific English (minimum level B2). Experience in scientific programming (Python, Julia, Matlab or similar
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CAD software like KiCad (or equivalent); - Confirmed programming skills, particularly in C and C++, as well as data analysis and processing using Python-based tools; - Ability to work in an
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of surface chemistry and catalysis, DFT and ML. She/he must be familiar with VSAP software and Python. She/he must be rigorous, curious and able to communicate in English. She/he must be proactive in
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expertise in NGS data analysis (WES, RNA-seq). - Experience with R and/or Python for statistical and bioinformatic analyses. - Experience in integrative multi-omics analysis. - Knowledge of biostatistics and
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Advanced programming skills in Python, with deep experience in libraries such as PyTorch, Pyomo, and the broader scientific stack (Xarray, Pandas). Knowledge of MATLAB is desired but not a requirement A
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flows, diffusion models). Hands-on experience in multi- and hyperspectral image processing (e.g., IDL/ENVI) and RTM inversion (e.g., ARTMO) Proficiency in scientific programming using Python, with
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in machine learning and/or advanced analytical methods, experience working with complex or large-scale datasets, and strong programming skills (e.g., Python or R). You will be able to communicate