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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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Eligibility criteria Selection will be based on the following scientific and technical criteria: • PhD in computational biology, machine learning, bioinformatics or a related field. • Proficiency with Python
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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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astrophysics, cosmology, or a related field completed by the start date; strong programming skills; working knowledge of machine learning applied to astrophysics and cosmology, in particular simulation-based
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Eligibility criteria - Education: Ph.D. degree in Robotics, Control, Optimization, Machine Learning, Computer Vision for Robotics, or related fields. - Technical Expertise: Strong background in optimization
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, statistics and scientific computing. * Proficiency in a scientific programming language, particularly Python, and the ability to develop reproducible processing procedures. * Knowledge of machine learning and
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decision systems for pricing and market strategies. In particular, algorithmic repricing tools allow real-time price adjustments based on predefined rules or machine-learning techniques. While recent
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This multidisciplinary thesis requires strong expertise in several of the following areas: Robotics, computer vision, control systems, dynamic modeling, signal processing, or machine learning
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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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data) will help validate observations and refine predictive models. Automated monitoring tools (scripts, dashboards, alerts) incorporating machine learning algorithms or statistical methods will be