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research in the current age lies in joining forces from multiple disciplines to focus on understanding causal and mechanistic links between the microbiome and chronic diseases alongside generalisable
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would be doing: Process and analyse large-scale calcium imaging datasets from multisensory experiments, including neural responses from visual and auditory cortices recorded over multiple days Apply and
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epidemiology, causal inference, genetic epidemiology, and machine learning. As a PhD candidate in the project, you will: Actively participate in group meetings, design statistical analysis plans in collaboration
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from multiple sources to estimate air quality, along with associated measures of uncertainty. Some traditional models can be relatively restrictive in nature and lack capabilities to deal with large
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application-oriented research that unlocks the potential of data through rigorous analysis – advancing solutions in societally relevant domains. Your profile Master’s degree in Statistics, Industrial
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and range shifts from mountains worldwide Participate in the development and adaptation of statistical models for analyzing the relationship between species distributions and climate Collaborate closely
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analysis and multivariate statistics (preferably using Python), to enhance building energy performance (and/or reduce building greenhouse gas) Experience in the full spectrum of research activities including
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, Cascais, Riga, Vilnius, Melsungen, Ciampino, Urla and Rhodes. The PhD project will involve: The use of data analytics (statistical models, machine learning, uncertainty quantification) to monitor and
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on the topic assigned for each position. Requirements: outstanding university degree (typically M. Sc.) in Computer Science, Data Science, Statistics, Mathematics or another relevant field study with good GPA
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multiple disciplinary approaches inclusive of SELEX technologies, bioinformatics, molecular and cell biology, molecular design, DNA nanotechnology, organic synthesis, immunology, and preclinical studies in