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this context. Already enrolled in a PhD programme. Minimum requirements: Knowledge of mathematics, machine learning, proficiency in programming languages including Python, C/C++. Experience with machine learning
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Education Institutions. Preference factors: - Knowledge of fundamental concepts related to energy management and gas networks; - Knowledge of optimization and forecasting models; - Knowledge of Python
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PhD Expert knowledge of statistical modelling, statistical inference and use of relevant software (e.g. R/Python) both for data processing, visualisation and programming. Excellent communication and
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years and in the relevant areas of Machine Learning / Artificial Intelligence, Credit Risk Modeling and Operations Optimization Modeling; The candidate must have strong programming skills in Python, and
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experience in in vivo large-scale two-photon calcium imaging Programming skills, Matlab, (and/or Python) Ability to communicate complex information clearly, orally and in writing Experience in
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quantitative policy tools. Strong programming skills (C++, R, Python, or similar). Experience working with large or longitudinal datasets. The ability to translate complex model outputs into actionable policy
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packages relevant to biostatistics and bioinformatics (e.g. R, Python, Fortran, Matlab, SAS, Stata or equivalent) and experience in the use of high-performance computer resources and parallel computation
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desirable. Proficient in Python, R, and ML libraries such as PyTorch or TensorFlow. Strong communication and collaboration skills; ability to work independently and as part of a team. Willingness to respect
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and data analysis tools commonly used in bioinformatics (e.g., shell scripting, Python, R). Solid understanding of omics data, including metagenomics, RNA-seq, and metabolomics. Experience with
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techniques. Training or demonstrated experience in remote sensing, spatial data collection, and thematic mapping. Proficiency in data analysis software (e.g., R, MATLAB, SPSS, Primer, Python). Experience with