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Field
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written communication skills in English are required. Other advantageous qualities include experience with coding (Python\Matlab) and numerical methods, as well as familiarity with concepts in complex
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of neuroimaging data, such as Quantitative susceptibility mapping (QSM), Magnetic Resonance Spectroscopy (MRS), or (resting state) network analysis. Data analysis and programming skills (Python, Matlab, R) and a
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, machine learning, and programming (preferably Python) is highly valued. Effective communication with clinicians and interdisciplinary researchers is crucial, and excellent proficiency in English is required
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programming skills (Python/C++); Proven interest in generative models and Explainable AI is a plus; Analytical and creative mindset, with the ability to work independently and in teams; Excellent command
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NLP, machine learning, and deep learning. Excellent programming skills (preferably in Python), with experience in fine-tuning language models and working with deep learning frameworks (e.g., PyTorch
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in the field of hybrid dynamical systems, observer design, learning techniques, and optimization. Knowledge of at least one programming language: Matlab, Python, is expected. Eager to work within a
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models. Experience (or strong willingness to learn) in programming and data analysis (e.g. Python, MATLAB, R, Fortran, or similar). Curiosity and motivation to work on fire emissions, air quality, and
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systems. Proven programming skills in Python, R, or a comparable language. Interest in developing methodologies to assess localized climate hazards, exposure, and vulnerability as inputs to impact-based
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Language Processing, Artificial Intelligence, Computational Linguistics or a related field; Strong background in NLP, machine learning, and deep learning. Excellent programming skills (preferably in Python), with
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or a related field. You possess solid knowledge of core web technologies. You have solid experience in Python and JavaScript programming languages. You preferably have experience with building and using