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theory, reinforcement learning, robotics and perception. You will investigate new algorithms and methods for robust and adaptive manipulation, including learning-based control for robotic systems and
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• Ability to give and receive scientific criticism in a constructive manner • A positive attitude and drive for learning skills, methods and theory • Ability to work in a structured manner, facilitating
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, primitives for quantum machine learning, and advanced aspects of learning theory. The work is both theoretical, i.e., formulating and proving theorems, and numerical, i.e., converting algorithms into code and
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interactions studies. The focus of the PhD-thesis needs to contain knowledge areas such as learning theory, cognitive theories with applications on studies of learning, design of learning environments, design
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interdisciplinary social science research on communication behaviors, including exploring how AI-assisted coding and analysis can reduce research barriers and expand research capacity for scholars. Position
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of the art (the project’s research field) Theory Methodology Work plan Results and hypotheses Bibliography Applications with a project description exceeding 1200 words will not be processed. The project
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learning and information theory, and expertise in at least one of the following areas is preferred: AI alignment, (differential) privacy, and coding or information theory for AI systems. Proficiency in
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health products, e.g. toothpaste. The simulations will utilize molecular dynamics and first principles DFT codes to model enamel surfaces and their interactions with the environment of the mouth. You will
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, e.g. in Python, particularly for machine learning Experience with machine learning theory, evaluation metrics, algorithmic fairness, statistics, or optimization is an advantage Language requirement
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• A positive attitude and drive for learning skills, methods and theory • Ability to work in a structured manner, facilitating teamwork • Ability to prioritise and complete work tasks • Ability