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candidate will join the Scientific Machine Learning group at TDB and SciLifeLab. The group develops theory, methods and software for data-driven science, with a current focus on uncertainty quantification
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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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and variation. The successful candidate will develop innovative methods and models to advance our understanding of genome evolution and variation. The position is based in the Computational Genomics
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and hardware design, and biophysical modeling. We do not expect applicants to arrive with expertise in all of these. What matters most is a demonstrated ability to learn new methods, a willingness to
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Umeå Plant Science Centre (UPSC) The Department of Plant Physiology is offering a postdoctoral scholarship within the project Con-TEki, which investigates the role of transposable elements (TEs) as
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binding with simulations and quantitative modeling to gain a physical understanding of life at the molecular level. We are recruiting a Postdoc that wants to use and develop state of the art methods
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experimental and computational methods to identify molecular mechanisms leading to dysfunctional cellular states in human disease (www.camunaslab.org ). The candidate will contribute to the improve Slice-seq, a
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Machine Learning group at TDB and SciLifeLab (Associate Professor Prashant Singh), which develops methods and software for simulation-based inference, generative models and robust machine learning, together
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methods are developed in parallel, the postdoc will develop systems and services that make biological data accessible to AI and computational tools, collaborating closely with the Human Protein Atlas (HPA
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biology: how sex chromosomes evolve, and what shapes sex ratios in natural populations. You will work with cutting‑edge methods in population genomics, bioinformatics and modeling, alongside a bit of hands