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for latent variables and their connections to modern machine learning. The project combines methodological research in statistics with applications to large-scale social science data. The successful candidates
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through a model-driven approach, i.e. a combination of simulation- and data-driven methods and tools with data analysis and machine learning as an important part. The work builds on established theories and
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Prof. Igor Zozoulenko). Our focus is on: Machine-learning accelerated materials simulations ML-accelerated simulation of ion and charge carrier transport in energy materials Multiscale modeling
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new methods for integrated sensing and communications in optical networks. Cutting-edge machine learning techniques for sensing data analysis, models of the impact of external phenomena on optical
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Experience in modelling of multiphase systems (e.g. liquid/solid, gas/liquid, liquid/liquid, or gas/solid) Experience in application of machine learning approaches. Experience in scientific computing
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data analysis and machine learning (e.g. XGBoost), including model interpretation techniques (e.g. SHAP). Very good oral and written proficiency in English. Excellent communication skills, ability
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, computer science, machine learning, or natural language processing, focusing on AI for Social Good or similar. Excellent written and spoken English is required, since the project is carried out in an international
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English language skills. Excellent collaboration and communication skills. Meriting factors for the position Experience with other types of computational methods, such as population simulations, machine learning
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https