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Strong written and oral communication skills Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork Preferred Qualifications Experience with block copolymer patterning
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in developing and optimizing infection models. In addition, the postdoctoral associate will have the opportunity to work in a collaborative environment, gain experience in studying polymicrobial
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the influence of these adaptations on interactions with other bacteria as well as pathogenesis in various infection models. The Postdoctoral Associate will be responsible for: Isolating DNA from longitudinal
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optimization schemes. From developing AI models to uncover structure-function relationships with limited data sets, to building automated electrode-electrolyte interface discovery workflows and implementing full
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of projects simultaneously. Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork. This position requires an on-site presence at the Argonne campus in Lemont
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modelling and faster digital technologies. We pride ourselves on closely aligning our teaching with the University’s research‑intensive ethos. Research‑led learning is embedded at every stage of our
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Postdoctoral Research Associate, Department of Basic Medical Sciences(College of Medicine - Phoenix)
are especially encouraged to apply. Extensive experience with animal models is required. Experience with iPSC-derived organoids, flow cytometry (FACS), and primary liver cell isolation is desirable but not
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models of sporadic low-grade glioma and Neurofibromatosis Type 1 (NF1)-related optic pathway glioma to accomplish these goals. We are additionally interested in how environmental exposures affect the non
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Strong quantitative analytical skills including but not limited to causal inference design, estimating and interpreting cross-sectional and panel models, and data visualization Ability to work with various
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‑classical algorithms to tackle domain‑specific, computationally demanding problems. Methods will be designed for near‑term (NISQ‑era) deployment, with models evaluated for expressivity, training stability