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, dissemination of research through publications, and mentoring PhD students. The labs’ extensive collaborative networks, both nationally and internationally, span various disciplines, offering diverse applications
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of national research infrastructures. The ideal candidates will have a PhD in a discipline closely related to computational social science by date of appointment (e.g. network science, computer science, data
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within the PIT CREW and National Center for Rural School Mental Health . Run advanced quantitative analyses using software such as Stata, R, Mplus, HLM, and/or SPSS. Generate research questions
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of renewal contingent upon satisfactory performance and the availability of funding. Minimum Qualifications: Education: PhD, MD, or equivalent degree is required at the start date. Experience: The ideal
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from the NIDDK and focuses on investigating the role that T1D risk variants play in islet autoimmunity through advanced single-cell techniques. The lab seeks an independent, highly motivated PhD-level
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. This position offers a unique opportunity to contribute to high-impact, interdisciplinary research at the intersection of network science, global research competitiveness, and generative AI capabilities
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data issues. Utilizing machine learning techniques as appropriate for data analysis. Developing computing programs and software to support research initiatives. Applying new methodologies to real-world
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university partners on systems-level issues. Run advanced quantitative analyses using statistical software. Generate research questions for analysis utilizing quantitative and qualitative data collected. Lead
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disease (CAD). You will apply expertise in data science, machine learning, as well as multi-omics integration to predict and validate functional regulatory networks in vascular cell types. This work will
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autoimmune diseases. QUALIFICATIONS Applicants must have PhD and/or MD (or equivalent) degree in hand by start date. Preferred applicants will have experience in immunology, metabolism, immunometabolism, and