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, Applied Mathematics, Computer Science, Statistics, Public Health, Economics or related computational field. Demonstrated experience in epidemiological methods, preferably using national mortality datasets
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related computational field. • Demonstrated experience in epidemiological methods, preferably using national mortality datasets and/or case-crossover analyses, and have a promising publication record
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associate to develop deep learning algorithms: 1) to model 3D protein complex structures and impact of mutations/PTMs on protein structure and interactions; and 2) to dissect functional elements (e.g
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Museum enriches learning opportunities for Cornell students by promoting object-based instruction and inquiry, as a component of coursework or independent study, in any discipline across Cornell’s schools
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applied to the development of sustainable terrestrial biotechnologies addressing the plastic crisis, carbon revalorization, critical metal recovery, and circular bioeconomy approaches on Earth and in space
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the plastic crisis, carbon revalorization, critical metal recovery, and circular bioeconomy approaches on Earth and in space. We are looking for an experienced scientist in bioengineering, molecular biology
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methods and software, including uncertainty methods, spatialized approaches, and integration with dynamic modeling frameworks. Experience applying or developing mechanistic animal models, and/or whole farm
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, and research method innovation. The collaborative expertise of the postdoctoral associate cohort, using both qualitative and quantitative methods, will contribute to, for example, foundational research
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. The postdoctoral associate will be expected to work both collaboratively and independently on research projects, advancing computational methods using machine learning, developing automated pipelines for data
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studies. Strong background in AI/ML fundamentals and extensive experience with deep learning (DL) methods. Demonstrated proficiency in Python and machine learning frameworks (e.g., PyTorch, Jax, scikit