Open date: July 27, 2022
Most recent review date: Friday, Dec 9, 2022 at 11:59pm (Pacific Time)
Applications received after this date will be reviewed by the search committee if the position has not yet been filled.
Final date: Saturday, Jan 27, 2024 at 11:59pm (Pacific Time)
Applications will continue to be accepted until this date, but those received after the review date will only be considered if the position has not yet been filled.
University of California, San Francisco
Institute for Neurodegenerative Diseases
We are searching for a biostatistics research specialist to start in Fall 2022. This position is available to study machine learning and biomedical imaging datasets such as high-content cellular screening (HCS) data and/or pathology whole slide images (WSI). The person in this position will work closely with our deep learning, image analysis, and relevant domain team members and established collaborators. This position will be part of a collaborative environment with access to state-of-the-art facilities in the Sandler Neurosciences Building on the UCSF Mission Bay campus.
This specialist will work on the processing and analysis of a digital bioimage dataset, the technical preparation of the dataset for machine learning (ML), and the deployment of trained ML models for active research and discovery. The work will progress through the use of the dataset to train, optimize, and critically evaluate convolutional neural network (CNN) models that score entire libraries of small-molecule compounds screened via existing HCS efforts by means of the multichannel microscopy images collected for each compound. We will focus in particular on screens that model neurodegenerative cellular phenotypes, such as the cell-to-cell propagation of alpha-synuclein and/or tau prion aggregates. A secondary alternative research need would instead be to analyze characteristic protein aggregates, such as formed by beta amyloid or tau, in neuropathological immunohistochemically stained WSI datasets, using a range of similar ML and analysis approaches.
The person in this position will be responsible for successfully performing analyses including the following methods: mathematical modeling, statistical learning, image analysis, unsupervised learning, data augmentation, k-fold validation, PCA or other dimensionality reduction, deep learning, semi-supervised learning, ROC and precision-recall curves, and data visualization.
• Specialists appointed at the junior rank must possess a baccalaureate degree (or equivalent degree) or at least four years of research experience (e.g., with instrumentation and research equipment, social science research methods, or creative activities).
• Specialists appointed at the Assistant rank must possess a master’s degree (or equivalent degree) or five years of experience in the relevant specialization.
• Specialists appointed at the Associate rank must possess a master’s degree (or equivalent degree) or five to ten years of experience in the relevant specialization.
• Specialists appointed at the full rank must possess a terminal degree (or equivalent degree) or ten or more years of experience in the relevant specialization.
• One-year time commitment, with possibility of extension.
• Demonstrated experience in python programming.
• Ability to work with pytorch, opencv, sklearn, pandas, and related python libraries.
• Background in mathematics, computer science, physics, and/or statistics.
• Experience in artificial intelligence / machine learning and/or data science.
• Candidates must meet the required qualifications at the time of appointment.
• Candidate’s CV or cover letter must state qualifications (or if pending) upon submission.
• Experience in neurodegeneration and/or cancer biology or medical training.
• Experience with live-cell imaging, digital pathology, or related image analysis.
• Experience with linux, virtual machines, and cloud or cluster processing.
• Experience with code versioning, management, tracking, and testing tools such as github and continuous integration.
• Experience with few-shot learning methods, convolutional neural networks.
Please apply online at https://aprecruit.ucsf.edu/JPF04040, with a CV and two references.
Appointees in the Specialist series will be expected to engage in specialized research, professional activities and do not have teaching responsibilities. Specialists are expected to use their professional expertise to make scientific and scholarly contributions and may participate in University and Public Service. Screening of applicants will begin immediately and will continue as needed throughout the recruitment period. Salary and rank will be commensurate with the applicants experience and training.
See Table 24B (https://www.ucop.edu/academic-personnel-programs/_files/2022-23/july-2022-salary-scales/t24-b.pdf) for the salary range for this position. A reasonable estimate for this position is $49,000-$173,900.
Curriculum Vitae - CV must clearly list current and/or pending qualifications (e.g. board eligibility/certification, medical licensure, etc.).
Cover Letter (Optional)
Statement of Research (Optional)
Statement of Teaching (Optional)
Statement of Contributions to Diversity - Please see the following page for more details: https://diversity.ucsf.edu/contributions-to-diversity-statement
Misc / Additional (Optional)
- 2 required (contact information only)
About UC San Francisco
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 that 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.