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Field
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and data analytics (including machine learning and deep learning); from high-performance computing to high-performance analytics; from data integration to data-related topics such as uncertainty
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mechanistic studies of microbiome-mediated pathogenesis. This is achieved by bridging microbiology and big data analytics in a structured doctoral training environment. The need of microbiome research in
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of topics is covered, from large-scale data management to data mining and data analytics (including machine learning and deep learning); from high-performance computing to high-performance analytics
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and improve computational methods for the pre-processing of MS data, exploring new algorithmic approaches for signal detection, deconvolution, and feature extraction. Machine Learning for Chemical
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software engineering, computer science, data science, bioengineering, bioinformatics, engineering, physics or related Experience in either machine learning or computational biology. Interest in both
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software engineering, computer science, data science, bioengineering, bioinformatics, engineering, physics or related Experience in either machine learning or computational biology. Interest in both
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processing and machine learning methods, and big data analytics solutions to extract highly accurate large-scale geo-information from big Earth observation data. Our team aims at tackling societal grand
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on behavioural syndromes and social networks in dogs and to some extent wolves. The selected PhD student will work with large-scale behavioural data sets using a range of approaches, including heritability
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conversational guides for enhancing visitors’ learning and experiences in public educational environments. The PhD student will focus on addressing the challenge of visual blindness in large language models (LLMs
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experience in one or more of the following areas: machine learning, reinforcement learning, algorithmic trading, or data-driven modelling. Excellent communication skills: Solid written and verbal communication