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they learn, PRL134, 147402 (2025). Qualifications We seek candidates with: A PhD in physics, applied mathematics, materials science, mechanical engineering, computer science, or a related field. Strong
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embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work
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related to endocrinology and/or genetics (preferred); the social skills to motivate people from different backgrounds and facilitate team-work; substantial and demonstrable experience with academic writing
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, and fairness attacks, as well as to increase the trust that their users have in these systems, while accounting for different phases of the AI life cycle, starting from data collection through training
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from the areas of few-shot learning, continual learning and modular deep learning, as well as different LLM alignment frameworks, based on reinforcement learning and direct preference optimisation
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the relativistic time dilation by only 1cm height difference in the gravitational field of earth. They are useful for searches of physics beyond the standard model, exploration of many-body physics, and societal
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outcomes under different market design scenarios. The research will combine machine learning, stochastic optimization, and agent-based modelling with behavioural experiments. Case studies from emerging
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, over 3,700 students and 425 staff members. For more information about the faculty, click here . What you bring In this team, we are looking for someone who can work independently. Every job is different