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mentorship from Dr. Max Lau, and opportunities to work with leading experts, including: Wei Jin (Computer Science) Ben Lopman (Epidemiology) Katia Koelle (Biology/Virology) Postdocs will be supported in
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teams. The Opportunity: Opportunity to work closely with computational colleagues to analyze, evaluate, and perform integrative computational analyses. Leverage multimodal high dimensional data to explore
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guidance for computational modeling of the data Publish research in peer-reviewed journals and present work at scientific conferences aligned with business objectives Demonstrate a high degree of
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therapy program to expand professional network and research experience. The postdoc may be based at either our North Chicago, IL or Cambridge, MA sites, and will work closely in a collaborative and
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that such institutions must be the recipient of a PREP award. The PREP program requires staff from a wide range of backgrounds to work on scientific research in many areas. Employees in this position will perform
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information about AbbVie, please visit us atwww.abbvie.com . Follow @abbvie onX ,Facebook ,Instagram ,YouTube ,LinkedIn andTik Tok . Job Description Program Overview AbbVie needs outstanding individuals willing
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Generate new research strategies to effectively address the needs of the postdoc project Collaborate with functional and technical experts to facilitate scientific achievement Maintain a high level of
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computational colleagues to build, train, and evaluate cutting edge AI models using large proprietary oncology datasets Leverage multimodal high dimensional data to investigate relationship between heterogeneous
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, or related approaches. Hands-on experience in high-throughput screening techniques for protein interactions and degradation pathways. Familiarity with computational tools for data analysis and bioinformatics
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potential, small angle X-ray scattering). Advanced computational and data analysis skills in biological systems. Familiarity with high-throughput experimental techniques and AI/ML applications in research