28 data-"https:"-"https:"-"https:"-"https:"-"Naturalis" Postdoctoral positions at KINGS COLLEGE LONDON
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AEP equivalent up to and including grade 7. Visit the Centre for Research Staff Development for more information. About you To be successful in this role, we are looking for candidates to have the
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Programme. You will work with a friendly, supportive, passionate, and hard-working group to undertake statistical analysis of quantitative data to test hypothesis on various aspects of mental health and
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and how this may be improved. This will include participant recruitment through various means, data acquisition (primarily through individual semi-structured interviews), data analysis, and working with
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further information about our Department, please visit our website . About the role The postdoctoral position will be an integrative part of an exciting ERC project dedicated to biosynthesize and
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team dedicated to advancing data-driven approaches to historical, linguistic, and cultural research. This post is part of the 5-year project Computational Corpus Annotation for Quantitative Analysis
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, clinical, imaging, and general population data in a multidisciplinary research environment. The postholder will work under the supervision of PI Prof Mitul Mehta (KCL) and Co-I Amir Englund (KCL), and also
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team dedicated to advancing data-driven approaches to historical, linguistic, and cultural research. This post is part of the 5-year project Computational Corpus Annotation for Quantitative Analysis
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lead analyses of large-scale datasets, applying advanced computational and statistical methods to integrate multimodal data (including MRI, MEG, EEG, and genomic data). The postholder will work with a
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are passionate about applying machine learning to real-world clinical challenges. The successful candidate will lead the development and validation of predictive models using multimodal data including neuroimaging
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critical minerals essential for clean technologies. By capturing the voices and silences of those most affected by extractive industries, the project aims to build a bottom-up, data-driven framework