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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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theory, statistical inference, and probabilistic modelling for uncertainty quantification in deep learning, particularly large language models. The focus will be on quantifying and evaluating uncertainty
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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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in molecular biology techniques, RNA-omics and/or proteomics, and related bioinformatic analyses. Fluency in spoken and written English, as well as a strong track record of carrying out and finalizing
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professional approach and analyze and work with complex issues. excellent command of English orally and in writing is required to publish in international journals. outstanding academic track record. skilled in
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cytometry. You have experience with phage display technology or similar combinatorial library technologies. You have an extensive track record of independent work. You have experience in the pharmaceutical
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excellence as evidenced by strong scientific publications and track record relative to career stage. Strong programming skills (Python or R) and familiarity with high-performance computing Preferred
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skills will focus primarily on merits within the subject area of the position. About the employment This position (in Swedish, “biträdande lektor”) is a tenure track position, and the qualification
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experience and track record in using open databases and bioinformatic tools and services. Strong programming skills for developing computational tools or data workflows (Python or similar), with good software