Larson Lab – Junior/Assistant/Associate/Full Specialist

Position Description

Larson Lab – Junior/Assistant/Associate/Full Specialist

The Larson Lab is seeking a Junior, Assistant, Associate, or Full Specialist. This position is focused on using modern machine learning and computer vision algorithms for automated large-scale analyses of cancer imaging data, working with Dr. Thomas Hope ( https://profiles.ucsf.edu/thomas.hope ) and Prof. Peder Larson ( http://www.radiology.ucsf.edu/research/labs/larson ).

More specifically, positron emission tomography (PET) with prostate-specific membrane antigen (PSMA) based agents is transforming the assessment of lethal advanced prostate cancers. UCSF and Dr. Thomas Hope have led the clinical translation of this agent, and a key next step is to develop advanced tools for analyzing PSMA PET data. This promises to maximize the utility by improving our understanding how the measurements in PSMA PET images in relation to diseases progression and treatments.

We propose to develop such advanced analysis tools using modern machine learning and computer vision methods to automatically segment and classify lesions as well as provide quantitative measures of localized disease burden.

The Responsibilities Of This Position Include

Curation and management of cancer imaging datasets Integration of information from clinical databases and cliniciansTraining machine learning models using cancer imaging datasets

Required Qualifications

Specialists appointed at the Junior rank must possess a baccalaureate degree in Computer Science, Electrical Engineering, Data Science, (or equivalent degree or related field) or at least four years of research experience.Specialists appointed at the Assistant rank must possess a master’s degree (or equivalent degree) or a baccalaureate degree with three or more years of research experience.Specialists appointed at the Associate rank must possess a master’s degree (or equivalent degree) or five to ten years of research experience.Specialists appointed at the Full rank must possess a terminal degree (or equivalent degree) or ten or more years of research experience.Must have strong written and verbal communication skills.Ability to work on collaborative research projects in a multi-disciplinary team.Applicants must meet all of the requirements by the time of hire.Applicant materials must list current and/or pending qualifications upon submission.

Preferred Qualifications

Experience with Python and PyTorchSetting up and curating large datasetsComputer vision model developmentTraining of Machine Learning modelsTraining of Machine Learning models based on image data

See Table 24B for the salary range for this position. A reasonable estimate for this position is $51,300-$181,800.

Please apply online at: https://aprecruit.ucsf.edu/JPF05026 with a cover letter, CV, and contact information for two references.

Document Requirements

Cover Letter Curriculum Vitae - CV must clearly list current and/or pending qualifications (e.g. board eligibility/certification, medical licensure, etc.). Statement of Research (Optional) Statement of Teaching (Optional) Statement of Contributions to Diversity - Please see the following page for more details: Contributions to Diversity Statement (Optional) Misc / Additional (Optional)

Reference Requirements

2 required (contact information only)

As a University employee, you will be required to comply with all applicable University policies and/or collective bargaining agreements, as may be amended from time to time. Federal, state, or local government directives may impose additional requirements.

UC San Francisco seeks candidates whose experience, teaching, research, or community service has prepared them to contribute to our commitment to diversity and excellence. The University of California is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age or protected veteran status.

San Francisco, CA