Neuroimaging Data Analyst Junior
University of Pennsylvania Perelman School of MedicineAbout the role
University Overview
The University of Pennsylvania, the largest private employer in Philadelphia, is a world-renowned leader in education, research, and innovation. This historic, Ivy League school consistently ranks among the top 10 universities in the annual U.S. News & World Report survey. Penn has 12 highly-regarded schools that provide opportunities for undergraduate, graduate and continuing education, all influenced by Penn’s distinctive interdisciplinary approach to scholarship and learning. As an employer Penn has been ranked nationally on many occasions with the most recent award from Forbes who named Penn one of America’s Best Large Employers in 2023.
Penn offers a unique working environment within the city of Philadelphia. The University is situated on a beautiful urban campus, with easy access to a range of educational, cultural, and recreational activities. With its historical significance and landmarks, lively cultural offerings, and wide variety of atmospheres, Philadelphia is the perfect place to call home for work and play.
The University offers a competitive benefits package that includes excellent healthcare and tuition benefits for employees and their families, generous retirement benefits, a wide variety of professional development opportunities, supportive work and family benefits, a wealth of health and wellness programs and resources, and much more.
Posted Job Title
Neuroimaging Data Analyst JuniorJob Profile Title
Database TechnicianJob Description Summary
Neuroimaging Data Analyst JuniorThe Translational Reproductive Neuropsychiatry and Data Science Lab is part of the Department of Psychiatry, the Neurodevelopment & Psychosis Section, and the Lifespan Brain Institute (LiBI). Our goal is to personalize early interventions in reproductive psychiatry. To do this, we use non-invasive neuroimaging and advanced computational methods to understand mechanisms underlying sex differences in psychopathology and identify neuroendocrinologic markers of risk vs. resilience for psychiatric disorders. The Neuroimaging Data Analyst Junior will provide support for managing and analyzing data.
Job Description
The Perelman School of Medicine at the University of Pennsylvania is the oldest and one of the finest medical schools in the United States. Penn is rich in tradition and heritage and at the same time consistently at the forefront of new developments and innovations in medical education and research. Since its founding in 1765 the School has been a strong presence in the community and prides itself on educating the leaders of tomorrow in patient care, biomedical research, and medical education. http://www.med.upenn.edu/.
Neuroimaging Data Analyst Junior
The Neuroimaging Data Analyst Junior provides support for managing and analyzing neuroimaging data. Analysts play a central role on large-scale projects that seek to understand sex differences in brain development and the biological basis of hormonally-driven psychiatric symptoms such as perinatal depression. Within these projects, they work in a team environment with a diverse, multi-disciplinary group of faculty and trainees from psychiatry, psychology engineering, statistics, and computer science. Primary responsibilities include management, processing, and analysis of high-dimensional imaging data. Additional responsibilities will be assigned based on the successful candidate's skills and interests. All analysts additionally lead independent projects (i.e., meta-analyses of neuroimaging studies) where they write first-author manuscripts.
Qualifications
The ideal candidate has a Bachelor's degree in Engineering, Statistics, or Computer Science, 0-1 years of research experience or equivalent combination of education and experience is required. To succeed in the highly collaborative environment, excellent interpersonal and communication skills are required. Program and data analysis skills are strongly preferred, including Python, linux/bash, and R. Experience with machine learning, clinically-acquired ne
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