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How much do associate applied science jobs pay per hour?

As of Sep 4, 2026, the average hourly pay for associate applied science in Pittsburgh, PA is $20.23, according to ZipRecruiter salary data. Most workers in this role earn between $15.87 and $22.64 per hour, depending on experience, location, and employer.

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An Associate in Applied Science (AAS) degree prepares graduates for technical and skilled roles such as medical technician, computer support specialist, dental assistant, or industrial technician. These jobs often require specific technical skills, certifications, and hands-on training, and they typically involve working in healthcare, technology, manufacturing, or service environments.

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Infographic showing various Associate Applied Science job openings in Pittsburgh, PA as of August 2026, with employment types broken down into 1% As Needed, 69% Full Time, 27% Part Time, 1% Temporary, and 2% Contract. Highlights an 97% Physical, 1% Hybrid, and 2% Remote job distribution, with an average salary of $42,076 per year, or $20.2 per hour.

Postdoctoral.Associate - Biostatistics and Health Data Science

University of Pittsburgh

Pittsburgh, PA

Full-time

Posted 8 days ago

Job description

The Department of Biostatistics and Health Data Science at the University of Pittsburgh School of Public Health, established in 1949, is a nationally recognized department with a longstanding tradition of excellence in biostatistics research and education. Faculty develop innovative statistical methodology and collaborate extensively on a broad range of biomedical and public health research. The department provides a highly collaborative and interdisciplinary environment, with opportunities for interaction with investigators across the School of Public Health, the University of Pittsburgh School of Medicine, the University of Pittsburgh Medical Center (UPMC), and UPMC Hillman Cancer Center.

The Department is seeking a highly motivated Postdoctoral Associate to work with Dr. Qiong Wu on methodological and applied research at the intersection of biostatistics, data science, and biomedical research. The position is supported by NIH-funded research projects and will provide opportunities to develop innovative statistical methodology and conduct scientific research using large-scale neuroimaging and real-world health data. The successful candidate will have opportunities to lead and contribute to methodological research projects, pursue independent research directions aligned with their interests, and collaborate with interdisciplinary investigators across the University of Pittsburgh and partnering institutions.

Responsibilities

The postdoctoral associate will conduct methodological and applied research addressing statistical challenges arising from complex neuroimaging and healthcare data. Research will span theoretical investigation, methodological development, computational implementation, and real-world applications. Areas of particular interest include federated and distributed learning, statistical data integration, evidence generation with real-world data, data harmonization, multimodal data analysis, and related topics, with applications to large-scale neuroimaging and clinical data, including electronic health records. The postdoctoral associate will work closely with Dr. Qiong Wu while having flexibility to pursue new research directions aligned with their interests and to contribute to ongoing research projects.

The postdoctoral associate will lead and contribute to methodological and applied research manuscripts, develop reproducible computational tools and software, and collaborate with multidisciplinary investigators on biomedical research projects. The position will provide a supportive environment for building an independent research portfolio and preparing for the next stage of the candidate's career.

Qualifications

Required

Applicants should have a PhD or equivalent in Biostatistics, Statistics, Data Science, or a closely related quantitative field by the start date. Candidates should have strong training in statistical methodology and computational and programming skills, preferably in R and/or Python. Excellent written and oral communication skills, the ability to conduct independent research, and the ability to work effectively in interdisciplinary collaborations are required.

Preferred

Candidates with experience or interest in federated and distributed learning, statistical data integration, causal inference, or related methodological areas, as well as in developing and applying statistical methods for neuroimaging and real-world health data, are especially encouraged to apply.

Application instructions

Applicants should submit:

  • Cover letter describing research interests, relevant experience, and career goals

  • Curriculum Vitae (CV)

  • Contact information for three references

Review of applications will begin immediately and continue until the position is filled.

Apply