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context. • Conduct statistical analyses, longitudinal modelling, or machine learning approaches as appropriate. • Develop documentation, codebooks, or tools to support reproducible research. • Lead
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comparative insights that enhance research conclusions from Hope observations. Develop Machine Learning methods and run numerical simulations on NYUAD’s High-Performance Computing (HPC) system. Support
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- 4 Additional Information Eligibility criteria Required skills: strong experience in TVB modeling, experience in fitting models to human data, strong level of autonomy, solid knowledge of machine
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, machine learning and deep learning. The project Motivation: Interpreting the genome means modeling the relationship between genotype and phenotype, which is the fundamental goal of biology. Achieving
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programming such as Python, R, MATLAB, or other similar programs and experience in using simulation/optimisation models and advanced data handling techniques e.g. machine-learning techniques, statistics
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learning environment. The instructor maintains accurate records, assists with program development and recruitment, and models the professional standards expected in the nursing field. This position is funded
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, particularly radionuclides, on a continental scale. The aim is to develop a new class of inverse Bayesian models, STE-EU-SCALE, combining innovative forward dispersion models, machine learning techniques, and
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materials according to the Lambert–Beer law, thus enabling an accurate description of PEC device behavior. In parallel, the coupling between kMC and CFD simulations will be achieved through machine learning
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-based ecosystem modeling, MRV/CDR, nature-based solutions, or food–energy–water systems. Experience with machine learning, remote sensing, or near–real-time environmental data systems. Experience
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samples. The candidate will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models, and to develop user-friendly tools that will be used by