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Field
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, is organised as a section at the Department of Geosciences. PHAB’s main goal is to develop predictive models to identify habitable planets around other stars. Within three different research themes: (1
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processes that are examined in a number of different fields of research, namely first and second language acquisition and processing and language change. Currently, each field employs different models
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outcomes Synthetic data generation (virtual patients) Statistical model checking to ensure statistical correctness of the results Machine learning–based classification and regression methods The candidate
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interpretable framework for tensor analysis. Specifically, the project will: Develop novel, modular statistical solvers to integrate domain-specific knowledge directly into latent variable models. Account for
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candidates within Modelling Strength and Failure in Recycled AluminiumAlloys funded through the Centre for Research-based Innovation SFI FAST – Future Aluminium Structures. The positions are linked
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models, and collaboration mechanisms to ensure efficient, reliable, and sustainable use of shared offshore energy resources. The successful candidates will be a part of a dynamic and internationally
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communities. The candidates will investigate how real-time data can support new operating models, and collaboration mechanisms to ensure efficient, reliable, and sustainable use of shared offshore energy
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large language models for measurement challenges (e.g., for small-sample calibration or for accelerated algorithms), (b) identifying and investigating aberrant response behavior (such as rapid guessing
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relevant if there is a strong focus on data-driven modeling, machine learning, and control. In any case, a documented background or experience in control is required. Your education must correspond to a five
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. The candidate shall take part in the research group on “Statistical models for high-dimensional and functional data ”, led by Professor Valeria Vitelli. Successful candidates will work on Bayesian models