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are seeking a candidate holding a Master 2 degree in computational biophysics, structural bioinformatics, or a related field. Knowledge of statistical mechanics and/or machine learning would be an asset
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will rely on: Developing machine learning-based surrogate AI models (physically informed neural networks) to predict the evolution of calcium carbonate precipitation rates associated with the reduction
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interest in data analysis, modelling, statistics, and machine learning. Experience in spatial data analysis (GIS), scientific programming (Python, R, or equivalent), or artificial intelligence will be
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protocols using computer-based data collection software, preparing and submitting ethics applications, drafting informed consent documents, and pre-registering studies. - Submit research protocols
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. These complementary datasets will be integrated using multivariate statistics and machine learning to identify microbial and chemical biomarkers of resilience and to reveal the interactions linking microbiome
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WDSs and soft skills for the researchers and professionals of the future. The following topics are included in these training chapters: • Artificial Intelligence and Machine Learning. • Explainable
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, Python, Bash). Good level on machine learning. Good level of written and oral English. Ease in a multidisciplinary environment, taste for teamwork, interpersonal skills. Scientific curiosity
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, biology, computer science or related disciplines Strong computational skills, including machine learning, e.g. demonstrable project in a relevant field A strong first-author publication record in a relevant
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computational fluid dynamics. • Experience in modeling, uncertainty quantification, or statistical methods. • Experience in data science or machine learning is considered an asset. • Experience with high
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strong background in optimization and machine learning. Good coding skills in Python, PyTorch are welcomed. Application Applications should contain a CV, a motivation letter, the grade records of the last