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–based, web-enabled decision-support tool for farmers, using machine learning (random forest) predictive models. Working closely with the PhD student and PIs to ensure rigorous model development
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learning. Job responsibilities will include: Develop simulation algorithms and software to model challenging gas adsorption behavior in porous materials Develop novel machine learning model for predicting
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porous materials Develop novel machine learning model for predicting gas adsorption behavior Investigate molecular transport and separation mechanisms for membrane process Publish journal articles and
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. Read more about CEBE (https://villumfonden.dk/en/nyhed/billion-kroner-research-grant-accelerate-green-transition-built-environment). The transition from CO2 producing building materials to renewable, CO2
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the Norwegian Institute for Nature Research (NINA) and partners in 14 countries. For more information, see: https://seatrack.net . This is a fixed termed position for 3 years in our section for terrestrial
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data to identify proteomic signatures and develop novel predictive models for Alzheimer’s, Parkinson, and Dystonia as well as to identify novel proteins and pathways implicated on disease pathogenesis
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Researcher to join an interdisciplinary project focused on improving early diagnosis and risk prediction in adolescents with early-onset psychosis, with particular emphasis on schizophrenia and bipolar
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using Matlab code to estimate the appropriate channel parameters in various wave bands for the design of radio systems. Collect the data and analyse it and relate it to weather data for prediction models
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The Duarte Group works at the forefront of computational chemistry, developing predictive tools for reaction modelling and molecular design across catalysis, sustainability, and health. Alongside
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integrating a wide range of neutrino and dark matter models, and aiming to evaluate their effects on large-scale structure statistics (LSS), as measured by the power spectrum and bispectrum of galaxies