(2024 Summer Intern) - gRED Computational Sciences (gCS) - Computational Biology and Translation, Digital Pathology
Friday 26th January 2024
Contact Email for the Job Posting firstname.lastname@example.org
Location South San Francisco
Title (2024 Summer Intern) - gRED Computational Sciences (gCS) - Computational Biology and Translation, Digital Pathology
Closing date Mar 15, 2024
Description We seek a motivated research intern to join the digital pathology group of the gCS Computational Biology and Translation (CBT) team within Genentech’s Research and Early Development (gRED) organization. Our team develops tissue based image analysis methods to extract disease biology insights, understand a drug’s mechanism of action on histopathology and support translational biomarker research programs for Genentech's oncology pipeline. The successful candidate will work closely with a team of imaging AI scientists, computational biologists and pathologists to develop and implement advanced AI algorithms to identify novel imaging-based biomarkers using digitized H&E and IHC-stained whole slide images.
Design, build and validate state-of-the-art digital pathology based AI models using clinical trial pathology imaging datasets to develop predictive biomarkers in oncology.
Document project progress, effectively summarize and present research findings in internal meetings.
Participate and thrive in an interactive, collaborative, and team-oriented culture.
Intensive 12-weeks, full time (40 hours per week) paid internship.
Program start dates are in May/June (Summer)
A stipend, based on location, will be provided to help alleviate costs associated with the internship.
Ownership of challenging and impactful business-critical projects.
Work with some of the most talented people in the biotechnology industry.
Who You Are
-Must be pursuing a PhD (enrolled student)
-Must have attained a PhD
Required majors: Biomedical Engineering, Computer Science, Bioinformatics, Applied Mathematics, Statistics or related discipline
Proficiency in machine learning, deep learning, computer vision and image analysis concepts
Proficiency in python programming to implement machine learning and deep learning libraries (such as Scikit-learn, OpenCV, Scikit-Image, PyTorch)
Proficiency in bioinformatics tools such as survival analysis and RNA sequencing is a strong plus
Prior experience with analysis of large complex pathology datasets (such as TCGA, tissue repositories with whole slide images) for machine learning applications is preferred.
At least one peer-reviewed first author publication/abstract ideally in digital pathology or oncology related journals/conferences (such as MICCAI, AACR, ASCO, USCAP, SPIE, SITC) is preferred.
Familiarity with or willingness to learn best practices for code development and maintenance, using Git for version control.
Excellent written and oral communication, collaboration, and interpersonal skills.
Relocation benefits are not available for this job posting.