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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
desirable. Candidates with experience in time-series machine learning, advanced data analytics, and programming (Python) are especially encouraged to apply. Special Physical/Mental Requirements Special
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analysis, time-series modelling, clinical prediction, risk modelling, survival analysis, causal inference, spatio-temporal modelling, or multimodal data integration. The candidate should be motivated by
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series of our coastal research projects enter a critical final or near-final phase which ends in autumn 2027. The researcher will carry out duties covering research, research management, and research
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against hourly wind and wave time series spanning the project lifetime, with Monte Carlo simulation over weather years and component time-to-failure. The models are currently implemented in MATLAB with
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Additional Information Eligibility criteria - Remote sensing (applied to tropical forests) - Knowledge of (tropical) climatology is an advantage - Statistical analysis of time series (correlations, composites
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closely related quantitative field in hand by the start of the appointment. ● Demonstrated expertise in molecular dynamics simulation and enhanced-sampling techniques (e.g., Gaussian accelerated MD/GaMD
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of the platform, as it will help them reduce the time needed to find proper employees or adapt their curricula to the requirements of the market. Based on the results of the analyses, a series of recommendations
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the use of the platform, as it will help them reduce the time needed to find proper employees or adapt their curricula to the requirements of the market. Based on the results of the analyses, a series of
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7 Sep 2026 Job Information Organisation/Company Universty of Aveiro Department SGRH Research Field Technology » Materials technology Researcher Profile First Stage Researcher (R1) Positions Postdoc
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equations, numerical analysis, mathematicalmodels in biomedicine, biostatistics, machine learning, statistical learning, multivariate statistics, time series,mathematical modelling, generalized linear models