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analyses is beneficial Expertise in machine learning and Bayesian statistics is beneficial What we offer: Work on a scientifically exciting, socially highly relevant, and globally visible ERC Synergy Grant
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/trait data) Conducting statistical modeling, feature selection, and predictive analytics for forest health, resilience, and biomass estimation Supporting data preprocessing, cleaning, normalization, and
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for candidates considering a future Ph.D. in accounting, finance, or related fields. Responsibilities Collect and clean large-scale datasets and conduct statistical analyses using Stata and Python. Review relevant
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such as environmental science, ecology, geography, engineering, and data analytics. Essential: A strong academic background (2:1 or above, or equivalent) in a relevant subject; Experience with statistical
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programming and quantitative analysis (e.g., statistics, scientific programming, numerical simulation) • Excellent written and spoken English (working language is English). • Ability to work independently
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guidelines and data protection regulations (e.g., GDPR) for handling data on minors. 2. Statistical and Predictive Modeling Knowledge in statistical analysis and data modeling (regression, mixed-effects models
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with statistics or programming in R/python/MATLAB desirable. Mode of study Full-time Start date 1 October 2026 Additional Funding Information This project is fully funded for 3 years. Funding includes
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health, epidemiology, statistics, biostatistics, or machine learning/artificial intelligence. You must have a strong academic background from your previous studies and have an average grade from your
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emotions Process data and conduct a range of advanced statistical analyses (including computational methods to analyse high-dimensional behavioural and physiological data) Write high-quality scientific
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therapeutic targets. The scholarship provides opportunities to apply statistical genetics approaches to dementia research within a collaborative and supportive research environment. Student type Future Students