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tools, wetlab approaches for circulating nucleic acids, interpretable machine learning for biomarker discovery, and methods we haven’t imagined yet, and to see those tools deployed in real patient cohorts
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world and a suite of associated machine learning tools. The incumbent will be advised by Dr. Laurel Symes (CAPS, [email protected]). Depending on the research direction, collaboration and additional
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at the intersection of AI and any humanities or social science discipline. Fellows will have the option to teach one undergraduate course each year. The first year of appointment will run 1 August 2027–31 July 2028
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are especially interested in candidates who are: ● Curious and eager to learn from diverse perspectives. ● Enthusiastic about tackling complex socio-environmental challenges. ● Skilled at building relationships
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project management skills, including the ability to work independently while coordinating with multiple collaborators. We are especially interested in candidates who are: ● Curious and eager to learn
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or understanding in reproductive biology, cell fate reprogramming and systems physiology is preferred but not required. • Ability to rapidly learn, critically evaluate, and integrate information across disciplines
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across learning sciences, computer science, machine learning, HCI and education research. Research Role Research themes for the NTO Postdoctoral Associate include, but are not limited to: Developing
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avenues of research rather than a required work plan. They may be pursued individually or in combination, and we welcome other creative and strategic approaches. Statistical or machine-learning approaches
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. Applications are welcome from scholars in relevant fields in the humanities and social sciences. The successful candidate will pursue an independent research agenda; teach one undergraduate course in each year
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and/or case-crossover analyses, and have a promising publication record. Expertise in one or more of the following areas: statistical analysis of large data sets, machine learning, data visualization