Larson and Hope Labs – Junior/Assistant/Associate/Full Specialist

University of California, San Francisco; San Francisco (CA); On-site; USD 57,000 - 201,700; Full time

Job summary

The University of California, San Francisco invites applications for a Junior/Assistant/Associate/Full Specialist in Larson and Hope Labs to advance AI methods for automated cancer imaging analysis, including PSMA PET segmentation and LLM-integrated pipelines.

The role collaborates with industry partners, develops deep learning models, manages large datasets, and contributes to publications and grants.

Qualifications

• Experience with medical image analysis and segmentation
• PSMA PET imaging experience is a plus
• Strong publication record or potential for first-author work
• Ability to collaborate with industry partners and clinicians

Responsibilities

• Develop deep learning models for medical image segmentation (PSMA PET and radiology)
• Create pipelines that integrate LLMs with image-based analysis for reporting
• Curate and manage large cancer imaging datasets with clinical/industry collaborators
• Publish findings in Radiology AI, Journal of Nuclear Medicine, and IEEE journals
• Contribute to grant applications and multi-site research projects

Skills

Imaging data handling

Deep learning

Multimodal AI

High-performance computing

Communication

Publication enthusiasm

Education

PhD in a related field

Bachelor's degree with research experience

Tools

MATLAB

Python

PyTorch

Linux/Unix

Job description

Position Description

Larson and Hope Labs – Junior/Assistant/Associate/Full Specialist

Larson and Hope Labs are seeking a Junior, Assistant, Associate, or Full Specialist. This position focuses on the development and application of cutting-edge artificial intelligence methods for automated analysis of cancer imaging data, with an emphasis on integrating large language models (LLMs) with image-based segmentation and interpretation algorithms. A primary application will be segmentation and quantitative analysis of PSMA PET imaging in prostate cancer, with additional projects spanning radiology-based AI applications across modalities.

UCSF has led the clinical translation of PSMA PET imaging, and this position represents a key next step in developing advanced computational tools to maximize its clinical and research utility. The successful candidate will also benefit from active collaborations with multiple industry partners, providing exposure to translational and commercial development pipelines.

Responsibilities Include

• Developing and validating deep learning models for medical image segmentation, with a focus on PSMA PET and broader radiology applications
• Designing and implementing pipelines that integrate LLMs with image-based analysis algorithms for enhanced interpretation and reporting
• Curating and managing large-scale cancer imaging datasets in collaboration with clinical and industry partners
• Disseminating research findings through peer-reviewed publications, with target venues including Radiology: Artificial Intelligence, Journal of Nuclear Medicine, and IEEE Transactions on Medical Imaging and related IEEE journals
• Contributing to grant applications and collaborative research projects across academic and industry settings

Required Qualifications

• Specialists appointed at the Junior rank must possess a baccalaureate degree (or equivalent degree) in computer science, electrical engineering, biomedical engineering, data science, or a closely 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 (or equivalent 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.
• Applicants must meet all the qualifications by the time of hire.
• Applicant materials must list current and/or pending qualifications upon submission.

Preferred Qualifications

• Scientific Computing programming experience (e.g. MATLAB, Python)
• Experience working with imaging datasets and image processing
• Experience with high-performance computing environments, including Linux/Unix, and running algorithms on CPUs and GPUs
• Deep learning frameworks, particularly Python and PyTorch
• Medical image segmentation and computer vision model development
• Large language models and multimodal AI architectures
• Setting up and managing large imaging datasets
• Strong written communication skills and a track record of, or clear enthusiasm for, first-author publication are essential
• PhD in computer science, electrical engineering, biomedical engineering, data science, or a closely related field is strongly preferred

See Table 24B for the salary range for this position. A reasonable estimate for this position is $57,000-$201,700.

Please click here to apply with cover letter, CV, and contact information for three 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)

Reference Requirements

• 3 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.

The University of California is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, protected veteran status, or other protected status under state or federal law.

UCSF is committed to welcoming and serving all people, honoring the dignity of every individual without preference or prejudice, in support of its public mission and in alignment with our PRIDE values and Principles of Community.

As a condition of employment, the finalist will be required to disclose if they are subject to any final administrative or judicial decisions within the last seven years determining that they committed any misconduct.

• “Misconduct” means any violation of the policies or laws governing conduct at the applicant’s previous place of employment, including, but not limited to, violations of policies or laws prohibiting sexual harassment, sexual assault, or other forms of harassment, or discrimination, as defined by the employer.
• UC Sexual Violence and Sexual Harassment Policy
• UC Anti-Discrimination Policy
• APM - 035: Affirmative Action and Nondiscrimination in Employment

San Francisco, CA