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learning and simulation-based inference for searches for dark matter (or other “invisible” new physics signals) at the Large Hadron Collider, with the support of competent and friendly colleagues in
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in large pre-trained models (vision-language models), generative models (flow matching, diffusion), simulation-based inference, and robust and active learning. The group has a wide network of
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higher education credits (ECTS). Relevant courses include, for example, image processing, computer vision, machine learning, deep learning and neural networks, as well as courses in Python, GPU programming
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through the department’s involvement in engineering and master’s programs. Our research and teaching are conducted within seven divisions with different research focus. Read more about us here About the
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described. The project has three main objectives: (1) Use simulation models to make inferences on the role of sex ratio selection on sex chromosome evolution in a different meiotic drive scenarios, (2
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if supporting documents are discovered to be fraudulent. Submission of false documents is a violation of Swedish law and is considered grounds for legal action. (A) and (B) can only be certified by