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May 2026 Apply now Are algorithms neutral tools, or do they actively shape the world they model? In this PhD, you will bridge the gap between building and critically studying Human-Centred AI systems
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the essentials of linear algebra, calculus, probability, statistics, algorithms, and data structures. The goal is to provide a foundational understanding of machine learning and deep learning methods
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, but this option can be time consuming and unsafe when testing complex algorithms (navigation, grasping . . . ). Using a simulator can speed up development, is safe, but breaks the natural interaction
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algorithms that can tightly integrate physical hardware, sensors, and computational models. This PhD position is centered on addressing these challenges through innovative computational methods, combining
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reinforcement learning methods can be used to solve multiobjective discrete and combinatorial optimization problems. The goal is to develop new algorithmic approaches that combine ideas from machine learning
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department in the ISSA expertise center that develops advanced AI solutions involving AI models, algorithms, implementations, sensors and hardware for small scale edge up to large scale distributed and hybrid
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explore unconventional ideas, develop computer algorithms for data analysis, create new experimental approaches, and apply the technique in areas like biomedicine, materials science, and geology. My group
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associate position to conduct research in machine learning (ML) for applications in High-Energy Physics (HEP). We seek highly qualified candidates with interest and experience in ML algorithms including
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three main themes: AI and Data Science Discrete Mathematics and Algorithms Logical Methods The School is particularly interested in supervising research that aligns with the work of the research groups
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proficiency in programming languages (e.g., Python, C/C++), knowledge of control algorithms and/or existing architectures (e.g., ArduSub, ROS2) for autonomous navigation, and the ability to develop innovative