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biology We expect a candidate with a strong background in machine learning or statistics. The candidate must also be proficient in high-level languages like Python. Familiarity with single-cell date and
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collaborate with ARCHIVES project partners to ensure coordinated progress and sharing of results. · Develop solutions combining numerical modeling, mathematical methods, and statistical/AI approaches
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research experience in music cognition, psycholinguistics, or ideally both, and a strong background in experimental design and statistical analysis. We are searching for someone highly motivated, interested
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integrating a wide range of neutrino and dark matter models, and aiming to evaluate their effects on large-scale structure statistics (LSS), as measured by the power spectrum and bispectrum of galaxies
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of current methods to detect balancing selection resulting from other mechanisms than HA, such as NFDS and FS using simulations. He/She will further explore new combinations of statistics, relying notably
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FieldPhysicsYears of Research ExperienceNone Additional Information Eligibility criteria - A PhD in mathematics/statistics/AI applied to ecological issues. - A strong publication record. - Proficiency in R and Python
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modeling and simulation, and statistical inference (lead by mathematicians and biologists) - The recruited postdoc will be asked to work in the labs on a daily basis. - The recruited postdoc will be expected
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Two-year postdoc position (M/F) in signal processing and Monte Carlo methods applied to epidemiology
-Negative Matrix Factorization will be explored. The second challenge is to leverage the derived statistical models to design automated data-driven procedures for the estimation of epidemiological indicators
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researchers with ample experience in MEG/EEG data analysis, BCIs, signal processing, deep learning for brain imaging analysis, biomedical statistics, dynamical systems and research on motor control. The lab has
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-resolved Lagrangian, Eulerian and pressure fields. This activity is inherently multidisciplinary with strong collaborations with other scientific fields, as applied mathematics or statistical physics. Fluid