APPLICATION INSTRUCTIONS: CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please login to Workday to complete the internal application process . Please do not apply here, apply internally through Workday. CURRENT PENN STATE STUDENT (not employed previously at the university) and seeking employment with Penn State, please login to Workday to complete the student application process. Please
do not apply here, apply internally through Workday. If you are NOT a current employee or student, please click “Apply” and complete the application process for external applicants . Approval of remote and hybrid work is not guaranteed regardless of work location. For additional information on remote work at Penn State, see Notice to Out of State Applicants . This is a term position; length of the term will be
discussed during the interview process. Continuation past the term length discussed will be based on university need, performance, and/or availability of funding. POSITION SPECIFICS Position: Assistant Professor of Medicine Department: Department of Medicine, Penn State College of Medicine Position Summary: The Division of Nephrology in the Department of Medicine at Penn State College of Medicine invites applications
for a non tenure line Assistant Professor in Medicine. We seek an innovative investigator with expertise in the application of epidemiology and machine learning methods in the study of cardiovascular-kidney-metabolic (CKM) syndrome, with a strong commitment to translational science and extramural funding. This position will play a critical role in advancing research in nephrology, supporting interdisciplinary
collaboration in the Department of Medicine. Responsibilities: The successful candidate will: Develop and sustain an independent, externally funded research program. Provide expertise in epidemiology, machine learning, predictive modeling and work with large data. Collaborate with investigators across nephrology, cardiology, endocrinology, and translational medicine. Contribute to graduate and medical education,
including mentorship of trainees and junior investigators. Participate in departmental service consistent with departmental expectations. Qualifications PhD in epidemiology or related field. Demonstrated expertise in use of epidemiology or machine learning theory in study of chronic disease conditions. Strong record of peer-reviewed publications. Evidence of current or potential extramural funding. Commitment to
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