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different dyadic motor coordination tasks. A range of neurophysiological measures (EEG, ECG and fNIRS) as well as behavioural measures will be recorded simultaneously from both partners. Machine-learning
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develop a new generation of hybrid models combining large-scale machine learning with physical knowledge to represent interactions between mobile robots and their environment. The research will address
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archaeological and historical contexts is also required. Additionally, the ability to perform *ad hoc* data processing (multivariate statistics, machine learning, etc.) is desirable. Proficiency in programming
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, machine learning, explainable artificial intelligence (XAI), digital twins, and integrated data-model approaches. • Study of the frugality of the developed approaches by reducing the requirements
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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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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