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machine learning frameworks is desirable. Interest in model predictive control, world models, foundation models, or learning-based robotics. Ability to work independently while contributing effectively to a
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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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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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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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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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. The successful applicant will develop a predictive pipeline using atomistic modeling and machine learning to identify optimal "seeds" for directing crystal growth, followed by rigorous experimental testing
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, seeks to recruit a junior research scientist to develop AI-enabled healthcare applications. Key Responsibilities: Develop and fine-tune computer-vision models, instance segmentation, and retrieval-based
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demonstrated track record in protein structure modelling methods, with hands‑on experience in protein or biologics design and engineering. Hands‑on experience with common machine learning / deep learning