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or water-related systems. • Strong proficiency in Python and relevant libraries for data analysis and modelling (e.g., TensorFlow, Keras, Scikit-learn, Pandas); knowledge of R would be an asset
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(e.g., Bioconductor, Galaxy, KEGG, Reactome, STRING). Proficiency in Python, R, and Unix/Linux-based environments for high-performance data analysis. Knowledge of biological network inference, causal
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of AI and Data Science : Machine and deep learning, NLP, BDI (Belief-desire-intention) systems, and Large Language Models (LLMs). Expertise in design and very good programming skills (Python, Pytorch
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the use of bioinformatics tools for metagenomic and transcriptomic data analysis (e.g., QIIME, DADA2, R, Python). Demonstrated ability to independently design and conduct experiments, analyze data, and
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optimal control theory. Proficiency in programming languages (Python, MATLAB) and experience with AI/ML frameworks (TensorFlow, PyTorch, Scikit-learn). Experience in data-driven modeling, deep learning, and
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methods. Programming languages, i.e. Python, Matlab, ... Level of experience evidenced by publications in peer-reviewed journals. Fluent in French and English. Experience of working as a member of a
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. Strong programming skills in languages like Python or R. Professional experience in the application of Machine Learning algorithms in the mapping and correction of spatial data. Professional experience in
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user skills in Python, MATLAB, LABVIEW or comparable technical computing and simulation environments Good English communication skills and the ability to work effectively both independently and within a
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, environmental sciences, geoinformatics, or a related discipline. Proficiency in advanced learning techniques and statistical modeling. Strong programming skills in languages like Python or R. Professional
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programming skills (e.g., C, C++, Python), familiarity with Linux Proficiency in English and ability to work in a team Outstanding analytical and problem-solving skills Employment terms: The successful