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Field
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Max Planck Institute for Demographic Research (MPIDR) / University of Manchester | Germany | 3 months ago
spend the first 21 months at the Social Statistics Department, University of Manchester. They will be working in a vibrant community of PhD students in Social Statistics, Social Data Analytics and
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independently under supervision and as part of a multi-disciplinary team Enthusiastic to grow and develop as a researcher Qualifications Research Associate Awarded PhD in Statistics or a closely related
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Qualifications* Requires PhD degree in Statistics + 2 years of experience supporting pain research: using statistical methods, including latent profile/class analysis, multivariate analysis, longitudinal models
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or examples to provide insights into the decision making process of complicated models. This family of explanations has both rich statistical and psychological grounding and the project will look to investigate
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will hold a relevant PhD/Dphil in statistics, machine learning or similar area, together with relevant experience working with brain imaging data and possess sufficient specialist knowledge in brain
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integrated with the NiMARE (NMA) software project. To be considered you will hold a relevant PhD/Dphil in statistics, machine learning or similar area, together with relevant experience working with brain
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, epidemiologists, oncologists, a surgeon, a radiotherapy physicist, and DPhil students. To be considered you will hold a PhD in statistics or a closely related field. You will have experience of managing research
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Parkinson’s Disease, or markers of brain structure and functioning, depending on the dataset. To do this, knowledge or willingness to be trained in advanced statistical modelling, ideally with an interest in
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UiO/Anders Lien 6th May 2025 Languages English English English Faculty of Social Sciences, Department of Psychology PhD research fellow Apply for this job See advertisement About the position
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. To do this, knowledge or willingness to be trained in advanced statistical modelling, ideally with an interest in methods for causal inference in observational data, is strongly preferred. Using various