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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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, computational biology, or bioinformatics with a heavy focus on machine learning and AI model training and development by the appointment start date. About 1 year of research or work experience in an academic
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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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). Applying advanced statistical and machine learning methods (e.g., predictive modelling, clustering, multivariate integration) to large-scale time series and sensor datasets. Contributing to the development
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simulations with machine learning models derived from experimental data. 1) Functional Knowledge and Technical Expertise (50%) a. COMSOL Modeling: Design, build, and run COMSOL Multiphysics simulations to model
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. The researcher is expected to have (i) strong machine learning skills to improve model performance and robustness, and (ii) exemplary passion and motivation to pursue multidisciplinary research at the intersection
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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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efficiency. Experience working with R or Python data science tools to analyze and visualize health care data and to develop data pipelines to support machine learning model development. Experience working with
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decision-making across diverse applications in computer vision and data analysis. Where to apply Website https://aunicalogin.polimi.it/aunicalogin/getservizio.xml?id_servizio=1079 Requirements Additional
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new programming languages, libraries and technologies. Any prior experience working with frontend/backend web development, machine learning, or high performance computing would be desirable, as would