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Associate Research Scientist / Post-Doctoral Associate in the Division of Science (Computer Science)
discovery, machine learning, and data science. The position will provide the opportunity to develop applied research skills in machine learning, interact with an international network of collaborators, and
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description Work on EU projects to develop next‑generation transport, emission and health forecasting models by integrating deep learning, xAI, and diverse data sources such as traffic sensors, smart‑card data
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biology and genomics. The position offers excellent opportunities for scientific publishing, international networking and academic career development. Our group studies the fundamental principles
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salaries, an array of benefits, an extensive support network, and above all, an enriching and highly collaborative working community that is deeply passionate about our vision for higher education, research
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project that involves designing microfabricated biomedical sensors and the readout circuitry for recording electrophysiology and electrochemical signals from biological samples. The overarching goal
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-reviewed academic journals and practitioner magazines; Present your research at national and international academic and practitioner conferences and actively develop your (academic and practitioner) network
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, comparative metabolomics, and MS/MS networking. Job Description: A postdoctoral Associate position in metabolomics and natural product chemistry is available as part of an NIH-funded project on nematode
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knowledge-guided machine learning, physics-informed machine learning, scientific foundation models, graph neural networks, geometric deep learning, operator learning, generative models, or AI agents
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, engineers, etc.) and have access to many experimental facilities and a network of international collaborations. Where to apply E-mail [email protected] Requirements Research FieldAllEducation
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, annotate and analyse metabolomic data using dedicated pipelines (construction of molecular networks, querying spectral databases, in silico annotation, calculation of metabolic diversity indices). Contribute