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groups. The theoretical methods include stochastic modelling, MD simulation, and Bayesian inference; the position will also include joint collaborative projects with other group members and our
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theoretically, in tight collaboration with experimental groups. The theoretical methods include stochastic modelling, MD simulation, and Bayesian inference; the position will also include joint collaborative
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perception at the landscape scale under different scenarios and employ behavioural experiments and Big Data analytics to understand how changes in time perception influence pro-environmental and sustainable
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strains with different aggression phenotypes, including BALB/cJ and BALB/cByJ mice, to link circuit function, harm-aversion, and pathological aggression. What you will be contributing As postdoctoral
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. This large multimodal dataset allows us to estimate and test different computational models of the decision and learning processes. One postdoc is currently working on the MEG and iEEG data, and one PhD
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. Experience with uncertainty quantification, Bayesian inference, inverse modelling, parameter estimation, or model calibration. Experience with high-performance computing, surrogate modelling, reduced-order
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to obtain and maintain a DOE Q clearance. Qualifications We Desire: Interest in developing neural-inspired and cutting-edge artificial intelligence algorithms (e.g., spiking neural networks, Bayesian neural
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carbon, nitrogen, and water flows in agroecosystems. A solid background in uncertainty quantification, applied statistics, Bayesian calibration, and Monte Carlo simulations. Strong skills in scientific
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with researchers across different disciplines. Optionally gain first-hand familiarity with the experimental side of the research, for those wishing to build a profile that bridges theory and laboratory
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also develop predictive models capable of characterising disease progression and forecasting individual outcomes in multiple sclerosis under different treatment exposures. Beyond your own research, you