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the robot's physical embodiment suffer from poor generalization, weak explainability, and limited transferability; (ii) sample-inefficient learning requires large volumes of annotated, domain-specific data; and
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Science, or a related technical field Master's or PhD degree in Machine Learning, Computer Vision, or related areas will be advantageous Preferred Qualifications: Experience with biological/ecological
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data from existing cohorts and national registries, applying novel machine learning methods. The specific work tasks will include data management of large studies, scientific work related to the topics
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Description This PhD position explores how AI agents can play games to generate meaningful gameplay data. You will work on reinforcement learning, automated feature engineering, and the comparison of AI- and
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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health
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-phd-positions/ . Requirements Top-ranked Master's degree in robotics, computer vision, system control, machine learning, mathematics, or a related field (background in any of the following); Being
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chair for ECCV2020, ECCV2024, and ICCV2025. His work has been published numerous times in top computer vision conferences and journals such as CVPR (x13), ECCV (x6), ICCV (x3), IJCV (x3) and PAMI (x1
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7 Jan 2026 Job Information Organisation/Company Czech Technical University in Prague Research Field Computer science » Cybernetics Computer science » Informatics Researcher Profile Recognised
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advantage: • Experience with health data standards (ICD, CPT, LOINC, SNOMED CT). • Familiarity with hospital workflows or clinical terminology. • Experience with machine learning, natural language processing
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machine-learning techniques; interpretation of multimodal patterns of brain organisation; collaboration with international partners in alzheimer prevention; contribution to methodological innovation in