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Postdoctoral Fellowship positions scholars to work with a Cornell faculty mentor and an external partner organization. Each Fellow receives individualized and cohort-level support in professional development
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applicant should have prior experience in data analysis, data visualization, and preferably the development of interactive web-applications through approaches such as Bokeh, Shiny, Taipy, Streamlit etc
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can PI proposals as well as participate in collaborative proposals) who will conduct independent and collaborative research and develop collaborations with faculty, staff, and students. You will have
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broader university. Synergistic professional development opportunities may include co-mentorship of research students in the lab and co-development of grant proposals. Along with mentored research
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research projects focused on in vivo metabolic tracing in mouse models. Perform metabolomic analyses to investigate metabolic pathways relevant to cardiac health. Engage in scientific communication, train
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contribute to the development of a proof of concept obtained at University Côte d’Azur for accessing the content of a metabolomics knowledge graph (KG) with a large language model. It is Python prototype of a
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candidate will work with Professor Guilio Del Zanna on producing advanced atomic models. This post offers an excellent opportunity to contribute to a high-profile project focused on the development
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combination of CFD modelling and running What-if-scenarios to support environmental decision-making and policy development with a focus on vulnerable coastal habitats in Ireland. The successful candidate will
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Postdoctoral position: Developing a human lymphoid organ-on-chip to evaluate candidate mRNA vaccines
A post-doctoral position is available to develop a 3D model of a human lymph node and apply this model to preclinical vaccine evaluation. The successful applicant will join the team of Lisa
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have the opportunity to develop independent research aligned with the aims of the ADN lab. Current work focuses on machine learning and multivariate decoding of neuroimaging data to predict subjective