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Uppsala University, Department of Information Technology Are you interested in probability theory, statistics, and mathematical modelling? Would you like to develop new methods for uncertainty
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Are you interested in developing mathematically grounded methods for uncertainty quantification in deep learning, particularly for large language models in healthcare applications? Are you looking
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computer science, image analysis and machine learning, engineering physics, data science, applied mathematics, molecular biotechnology engineering, or another related field; or Have completed at least 240
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requirements, the applicant must have credits in Life Science, Computer Science Mathematics, Physics or Bioinformatics or alike, including a 30 credit Degree Project (thesis). proficiency in English equivalent
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analysis to better understand how molecular and cellular processes are coordinated across cells, tissues, and organ systems in human health and disease. You will be responsible for developing and applying
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states and structural ensembles. The project builds on extensive expertise in the Piazza laboratory in quantitative proteomics, and proteome-wide analysis of protein structural changes. The PhD student
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, or a related field, who enjoys interdisciplinary work spanning wet-lab experimentation and computational data analysis. In addition to the aforementioned requirements for the position: A Master’s
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experience in population genomic modeling (e.g., using SLiM), analysis of structral variants from long‑read data, population genomic analysis of whole‑genome re-sequencing data are a merit — these techniques
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methods for detailed analysis of different RNA molecules in blood samples and contribute to a new research field with strong clinical potential. What you will work on The successful candidate will be