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: Stochastic Analysis Appl Deadline: 2026/03/16 03:59 AM UnitedKingdomTime (posted 2026/01/29 05:00 AM UnitedKingdomTime, listed until 2026/04/01 04:59 AM UnitedKingdomTime) Position Description: Apply Position
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in statistics and stochastic modeling with applicability in microeconomics, macroeconomie and sustainability are particularly welcome, Experience in previous similar projects will be considered a major
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experience in statistics and stochastic modeling with applicability in microeconomics, macroeconomie and sustainability are particularly welcome, Experience in previous similar projects will be considered a
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drift, environmental stochasticity, and double crossovers). • Study the complex feedback loops between the evolution of genetic load within inversions and the spatial dynamics of multiple inversions
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duration in gene regulation — bridging statistical physics, stochastic processes, and real biology. Job description During the development of an organism, gene regulation allows proteins to be expressed so
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physics, stochastic processes, statistical inference, and epidemiology, with SARS-CoV-2 and influenza as key case studies. Your job In this project, you will develop a quantitative theory of evolution
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optimization-based updates (e.g., stochastic gradient methods and Bayesian learning), Probabilistic performance guarantees, leveraging tools from stochastic systems, RKHS-based learning, and Bayesian inference
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-relevant properties such as thermal stability, controlled stochasticity, switching dynamics and compatibility with neuromorphic architectures. The goal is to build and validate an automated multiscale
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), Computational Biology (stochastic and analytical models of gene expression), Signal Processing (machine learning, image and signal processing), Biophysics, Microbiology and Single-cell Biology (flow cytometry
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. For more information, please see https://www.scilifelab.se/data-driven/ddls-research-school/ Background and description of tasks Our group develops new single-cell multiomic methods to characterize microbial