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Field
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addition to carrying out bench research using electrophysiological recording, the successful candidate will be expected to help develop data management software for managing and analyzing large volumes
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-grade codebases, including data pipelines and analysis workflows. Developing and deploying AI agents and large language model–based tools for tasks such as document processing, information extraction
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The position is funded by the Open Philanthropy grant “Verifiably Robust Conformal Probes”. The project’s goal is to develop methods for latent probing (aka activation monitoring) of large language models (LLMs
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about to obtain* a PhD (awarded / submitted*) in health data science, epidemiology, medical statistics, or a closely related area such as computer Science, or mathematics (* If PhD pending, it must be
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on respiratory disease in a longitudinal perspective. It will be based on data from a large cohort of individuals extending back to 1990 from the Respiratory Health in Northern Europe (RHINE) study, as
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molecular and phenotype data. Including code that is production-ready for dissemination to other laboratories and for diagnostic use, and the management of large-scale data. They will join a team of
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interventions work best for whom, and how support systems can become more equitable and inclusive. This PhD position has an education and society-oriented focus. The PhD Fellow will work with large-scale register
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processes. Projects can include assembling, sharing, integrating, and advanced analysis of large amounts of data from diverse sources, including experiments, observations, and simulations, to gain a deeper
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of optimizing pipelines for large-scale genomic projects. Special Instructions Required documents: CV Research summary of PhD work. Cover letter describing your interest in the lab and initial ideas for new
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, clean, and analyze large-scale datasets combining financial, environmental, and policy information, including both structured and unstructured data sources. Apply artificial intelligence and large