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(or equivalent) in data science, applied mathematics, electrical engineering, or a related field. • Strong background in machine learning and/or statistical modeling. • Good written and oral communication skills
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of innovative multicriteria models for the management, integration, and enhancement of geological and geophysical data, using geostatistical, geomatic, and, where appropriate, machine learning techniques
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(EMG), to capture detailed motion, interaction forces, and muscle activity. Predictive Physiological Modeling: Development of machine learning models capable of anticipating motion intent while
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uploaded using the dedicated electronic form. helpdesk: petra.koudelova@fsv.cvut.cz Physics-guided learning for machine control Description: Robust machine control assumes modeling of robot-environment
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., PyTorch, TensorFlow, HuggingFace). Model Development and Delivery Support Perform data cleaning, exploratory data analysis (EDA), and feature engineering. Train, evaluate, and compare machine learning
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molecular docking, molecular dynamics and free-energy methods (MD/FEP), machine learning for molecular design, and protein–ligand modelling. Experience bridging computational and experimental groups, and the
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-scale vision/language/action models) can guide: World models for learning predictive representations of system dynamics Model Predictive Control (MPC) for robust decision-making under uncertainty Robotic
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and image analysis within the project, responsible for designing and iterating on machine learning architectures, managing training pipelines and datasets, and optimizing models for deployment across
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et d'assurer la stabilité des performances dans le temps. Cette thèse s'inscrit dans le cadre de l'apprentissage continu, un domaine émergent du machine learning, qui vise à concevoir des modèles
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will produce a dataset on social interactions aimed at training machine learning models for human-robot interactions. Robot decision using internal simulations : As mentioned at the beginning of