Question 1

What qualifications are required for this Assistant Professor position?

Accepted Answer

Candidates must hold a Ph.D. , M.D./Ph.D. , or equivalent in computer science , engineering , mathematics , statistics , biostatistics , bioinformatics , or related quantitative fields. Expertise in artificial intelligence and machine learning applied to digital pathology or image processing/analysis is required, with Pathology training or research experience preferred. See faculty jobs for similar roles.

Question 2

What teaching responsibilities does this role involve?

Accepted Answer

Teaching includes instructing and overseeing medical students , graduate students , post-doctoral students , residents , and fellows . Explore lecturer jobs or professor jobs for more on academic teaching loads.

Question 3

Are clinical responsibilities required?

Accepted Answer

Clinical duties in laboratory medicine or anatomic pathology may apply based on training, but are not required . Focus is primarily on research and teaching in AI for digital pathology . Check research jobs for non-clinical options.

Question 4

How do I apply for this tenure-track position?

Accepted Answer

Apply via Interfolio by the deadline of Jan 29, 2026, 11:59 PM ET . Position opened Jan 29, 2024. Review academic CV tips to prepare.

Question 5

What research opportunities are available?

Accepted Answer

Develop an independent program in image analysis/machine learning for digital histopathology . Collaborate with CBICA , AI2D , Radiology, and Biomedical Informatics. Guide informatics infrastructure for pathology data with EHR/genomics. View postdoc jobs for related paths.

Question 6

Does the position support diversity and inclusion?

Accepted Answer

The University seeks candidates embracing diversity . EOE encouraging minorities/women/individuals with disabilities/protected veterans . Learn more via employer branding in higher ed .

University of Pennsylvania

Philadelphia, PA 19104, USA

"Assistant Professor, Tenure Track Artificial Intelligence in Digital Pathology"

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