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. Starting position for grantees will be March-April 2027. PhD candidates are welcome to apply but they must obtain their PhD degree by the end of 2026. Fellowship is for a duration of 24 to 36 months
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new pollen analysis • Modeling and Statistics: Utilize GIS, R statistical environment, and other tools for pollen-based modeling (REVEALS and LOVE models) and statistical analysis. • Collaboration: Work
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- 4 Additional Information Eligibility criteria Essential skills - PhD in a relevant field (cognitive neuroscience, psychology, linguistics, psycholinguistics) - Experience in EEG and/or fNIRS
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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 experience
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hybrid electrodes (tetrodes & field potentials) in human data Perform the pre-processing, time-frequency analyses, statistical analyses and modelling necessary to link neural characteristics to decision
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a modeling approach inspired by statistical physics to describe individual strategies, their interactions, and emergent effects at the group scale. The candidate will contribute to the development and
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that incorporates a broad range of neutrino and dark-matter models, assessing their effects on large-scale-structure (LSS) statistics as measured by the power spectrum and bispectrum of galaxies or intensity maps
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contract – 2 years Candidates must hold a PhD, in Industrial Ecology, Process Engineering or Mining Engineering, with demonstrated link/application to the mineral raw materials sector. The candidates should
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, which combines tools from probability, statistics, PDEs, and extreme-value theory to address climate-related questions. Qualifications: Completed PhD degree in Mathematics, with a solid background in at
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to carry out the following tasks: Development of analysis scripts for the preprocessing and automated processing of functional neuroimaging data; Statistical modeling of imaging data and evaluation