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%). You will work at the intersection of numerical analysis, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model
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internationally outstanding research in the life sciences. Project description We seek two highly motivated postdoctoral researchers to develop new mathematical and computational methods for modeling developmental
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nanoplastics act as environmental mutagens that contribute to cancer initiation and progression? Using prostate cancer as a model disease, the project investigates whether nanoplastics directly interact with DNA
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holographic models, and applying them to shed new light on the physics behind black hole horizons and spacetime singularities. Matrix theory is an important approach to non-perturbative string theory in which
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model and biological membranes. The experimental data will be paired with results from molecular dynamics simulations to provide a complete characterization of the biophysical properties of the imaged
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development, participatory visioning, or pathway modelling, and familiarity with the concept of tipping points. Demonstrated ability to integrate qualitative and quantitative methods and work across disciplines
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at the intersection of numerical analysis, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model reduction, with
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Chemical Biological Centre (https://www.umu.se/en/kbc ) at Umeå University and is affiliated with the national Centre of Excellence – Umeå Centre for Microbial Research (UCMR) (https://www.umu.se/en/ucmr
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proliferation and adaptations. The postdoctoral fellow will lead the development of high-fidelity EV workflows (from plasma and cell models) and establish quantitative nanoparticle- and flow-cytometry-based
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: October 2026 Full call details, eligibility criteria, application templates, and a matchmaking platform for identifying potential supervisors are available at: https://www.scilifelab.se/data-driven/ddls