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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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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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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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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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of the underwater environment and marine installations. The system must be flexible, distributed, robust, energy-efficient, cost-effective, and secure, and be able to handle large volumes of data. Acoustic
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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
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background, interests, and career goals align with its objective PhD diploma or a letter/information indicating the expected defense date Transcript of all modules and results from university-level courses