| Request ID | Type | Request Status | Requester Name | Requester Email | Requester Email Copy | Skills | Knowledge | Description of Request | Skills/Experience | Start Date | Availability | End Date | Flexible Dates | Additional Information | Apply | |
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| 1 | TC0162 | Research Project | Open | Zoe Quandt | Zoe.Quandt@ucsf.edu | Zoe.Quandt@ucsf.edu | HPC/GPU ComputingPythonR | Machine Learningnon-ML Statistics | My lab focuses on endocrine side effects of cancer immunotherapy using a multimodal approach. I am looking for people with experience with programming in R and/or python to support analysis looking for novel autoantibodies and genetic risk. | Experience with use of statistical programs such as R and python are required. Statistic experience is a plus. | 25% | Yes | People who can dedicate 25% effort or more for at least a semester would be wonderul. | https://app.smartsheet.com/b/form/0196d62f5a067732922e38b82707d53b?Request%20ID=TC0162&Type_ID_2=2 | ||
| 2 | TC0163 | Research Project | Open | Minnie Sarwal | minnie.sarwal@gmail.com | minnie.sarwal@gmail.com | Git/GitHubJQuery/JavaScriptMLib/Machine LearningPythonR | Machine Learning | Omic analysis of kidney transplant samples already conducted in the lab and to utilize this and public data coupled with spatial imaging data analysis to better understand the pathobiology of immune injury and rejection and to utilize this information to conduct drug repurposing studies to identify new drugs for treatment of CMV disease and graft rejection | Archival data and sample sets in lab and deep expertise in lab and with PIs to support immunology based and statistical analysis of kidney transplant rejection with Pi’s in transplant and computational biology | 01/30/26 | 50% | 01/30/27 | Yes | Would lead to publication and funding application | https://app.smartsheet.com/b/form/0196d62f5a067732922e38b82707d53b?Request%20ID=TC0163&Type_ID_2=2 |
| 3 | TC0164 | Research Project | Open | Edilberto A.orim | amorim@ucsf.edu | amorim@ucsf.edu | Apache SparkGit/GitHubHPC/GPU ComputingMLib/Machine LearningNLP ToolsPyTorchPythonRSQLTensorFlow | Machine Learningnon-ML Statistics | Our group focuses on large scale deep phenotyping of patients with acute brain injury. This includes quantitative and machine learning work using continuous invasive and non-invasive EEG, brain MRI, and high-resolution electronic health record data. | Familiarity programming (python, MATLAB, or R depending on interests) | 01/02/26 | 90% | 12/31/26 | Yes | Visit our website: https://alab.ucsf.edu/ | https://app.smartsheet.com/b/form/0196d62f5a067732922e38b82707d53b?Request%20ID=TC0164&Type_ID_2=2 |
| 4 | TC0165 | Research Project | Open | Sharat Israni | sharat.israni@ucsf.edu | sharat.israni@ucsf.edu | Git/GitHubPythonSQL | Biologynon-ML Statistics | Based on student interests, we can offer opportunities for a variety of projects that help advance UCSF's Knowledge Computing platform. Students will work to develop an integrated biomedical summarizer, i.e. an interface to connect and summarize information about a biomedical entity from multiple knowledgebases. Some services (e.g. Bioportal) create these connections via ontologies, but results are not returned in a programmatic (AI-ready) way or in a user-friendly, summary form. Our team has worked to develop a comprehensive knowledge network and other tools that accomplish pieces of this, but would like to put it all together into a pipeline that serves a specified use-case (which can be suggested by the project team or specified by our faculty). | Undergraduate level knowledge of computer programming & databases is required, along with an interest in and basic understanding of biomedical concepts. Students will learn how to model patient data from electronic health record systems and other related biomedical data repositories; and how to use machine learning tools for prediction, design, and/or development of models. Students will develop their practical programming skills (Python, SQL, and other tools) and gain familiarity with connecting multimodal data types utilizing standard biomedical ontologies. | 10% | Yes | https://app.smartsheet.com/b/form/0196d62f5a067732922e38b82707d53b?Request%20ID=TC0165&Type_ID_2=2 | |||
| 5 | TC0166 | Research Project | Open | Elise Harb OD PhD | elise.harb@ucsf.edu | elise.harb@ucsf.edu | Git/GitHubPythonR | non-ML Statistics | Our work investigates the role of the visual environment (habitual indoor and outdoor activities, e.g. device use, nearness of objects, lighting characteristics) of a child and how it relates to the development and/or progression of myopia (nearsightedness). Myopia is rapidly becoming more prevalent and has tremendous economic, social and disease burden. This project has a tremendous amount of visual environment data from wearable technologies worn by kids which needs dynamic analysis | - Proficient coder (Python, R, Matlab) - Interest in doing translational research | 35% | Yes | https://app.smartsheet.com/b/form/0196d62f5a067732922e38b82707d53b?Request%20ID=TC0166&Type_ID_2=2 | |||
| 6 | TC0167 | Research Project | Open | Everlyn Kamau | everlyn.kamau@ucsf.edu | everlyn.kamau@ucsf.edu | Git/GitHubPythonR | non-ML Statistics | Developing a simple and open-source digital tool for reproducible reporting and data interpretation. This project will write analytical workflows and consolidate them plus any associated data into an application or a digital tool to be hosted online and shared freely as a file. Our work has been the basis of new WHO guidelines for use of serology to inform trachoma elimination endgame. This project contributes to continued efforts towards trachoma control and elimination, with potential for wide-reaching impact in NTDs. Epidemiology, global health, disease surveillance | - Coding in R, Python, including writing markdown and jupiter notebooks - Unix/Linux command line - Shiny frameworks - Git / GitHub | 06/01/26 | 100% | Yes | Laptop / computer will NOT be provided. Self-drive and highly motivation with an interest in contributing to global health research and infectious diseases. Location of project - UCSF Mission Bay campus | https://app.smartsheet.com/b/form/0196d62f5a067732922e38b82707d53b?Request%20ID=TC0167&Type_ID_2=2 | |
| 7 | TC0169 | Research Project | Open | Meir Marmor | meir.marmor@ucsf.edu | meir.marmor@ucsf.edu | PythonR | Machine Learningnon-ML Statistics | A study examining outcomes in patients with acute compartment syndrome who were found down (with or without suspected trauma). Specifically, we aim to assess whether a longer time from hospital admission to fasciotomy is associated with worse outcomes, measured by length of stay and readmission rates. | Data science skills | 10% | Yes | https://app.smartsheet.com/b/form/0196d62f5a067732922e38b82707d53b?Request%20ID=TC0169&Type_ID_2=2 | |||
| 8 | TC0170 | Research Project | Open | Meir Marmor | meir.marmor@ucsf.edu | meir.marmor@ucsf.edu | PythonRSQL | Machine Learningnon-ML Statistics | A study evaluating how social determinants of health influence postoperative outcomes after fracture surgery, including length of stay, opioid prescription refills, and hospital readmissions. | Data Science | 10% | Yes | https://app.smartsheet.com/b/form/0196d62f5a067732922e38b82707d53b?Request%20ID=TC0170&Type_ID_2=2 | |||
| 9 | TC0171 | Research Project | Open | Peng He | peng.he@ucsf.edu | peng.he@ucsf.edu | Git/GitHubPython | Biologynon-ML Statistics | The He Lab at UCSF seeks master’s students interested in single-cell genomics, computational biology, and human disease. Our research uses single-cell RNA sequencing and computational genomics to build and integrate large-scale cellular atlases that help us understand human development, blood biology, lung biology, and cancer. Students will participate in data analysis, atlas integration, and interpretation of gene expression programs across tissues and disease contexts. We welcome students with interests in quantitative analysis, programming, or biology who are motivated to work at the interface of computation and biomedical research. | python biological data interpretation linear algebra | 03/01/26 | 5% | 06/30/26 | Yes | https://app.smartsheet.com/b/form/0196d62f5a067732922e38b82707d53b?Request%20ID=TC0171&Type_ID_2=2 | |
| 10 | TC0172 | Research Project | Open | Anil Kamat (Ashish Raj) | anil.kamat@ucsf.edu | anil.kamat@ucsf.edu | MLib/Machine LearningNLP ToolsPyTorchPythonTensorFlow | BiologyMachine Learningnon-ML Statistics | The Raj Lab invites motivated master’s students to join an interdisciplinary research project at the intersection of advanced machine learning and computational neuroimaging. This work explores emerging AI methodologies to model high-resolution brain patterns associated with neurodegenerative processes using multimodal imaging data (MRI, Tau-PET, Tissue, Biomarkers, EEG, etc). By combining structural and functional information with modern generative and coordinate-based learning frameworks, the project aims to develop computational tools that enable more continuous and anatomically detailed representations of brain changes than traditional approaches. Students will contribute to the design and evaluation of generalizable AI models, the integration of multimodal biomedical datasets, and the analysis of high-dimensional brain representations in the context of disease-related variation. | Deep learning, Statistics, Computer vision, or Computational neuroscience | 25% | Yes | https://app.smartsheet.com/b/form/0196d62f5a067732922e38b82707d53b?Request%20ID=TC0172&Type_ID_2=2 | |||
| 11 | TC0173 | Research Project | Open | Adam Ferguson | Adam.ferguson@ucsf.edu | Adam.ferguson@ucsf.edu | R | BiologyMachine Learningnon-ML Statistics | For this project, our interest in neurotrauma, specifically if spinal cord injury (SCI) is associated with an increased risk of developing acute kidney injury (AKI) compared to traumatic brain injury (TBI) and non-neurotrauma trauma control patients. | Background in data science, SCI, TBI, polytrauma. | 5% | Yes | One of my postdocs, Jason Gumbel, will be heavily involved and will be able to dedicate more than 5% effort (closer to 20%-40%) | https://app.smartsheet.com/b/form/0196d62f5a067732922e38b82707d53b?Request%20ID=TC0173&Type_ID_2=2 | ||
| 12 | TC0174 | Research Project | Open | Riley Bove | riley.bove@ucsf.edu | riley.bove@ucsf.edu | Git/GitHubMLib/Machine LearningNLP ToolsPyTorchPythonTensorFlow | Machine Learningnon-ML Statistics | Large digital phenotyping dataset of >500 individuals with and without neurological conditions. Data include gait metrics, videos (gait, face, dexterity), speech and cognitive assessments. Multiple opportunities to utilise ML to derive relevant clinical indices to categorise by disease state or monitor treatment response and progression in a single modality over time. | Must have proficiency in Python, data science and ML libraries (NumPy, Pandas, Matplotlib, Scikit-Learn, and either TensorFlow or PyTorch). Ideal candidates will have prior experience working with computer vision libraries such as OpenCV or the ability to quickly learn these APIs. | 02/25/26 | 20% | 01/01/28 | Yes | https://app.smartsheet.com/b/form/0196d62f5a067732922e38b82707d53b?Request%20ID=TC0174&Type_ID_2=2 | |
| 13 | TC0175 | Research Project | Open | Urmimala Sarkar | urmimala.sarkar@ucsf.edu | urmimala.sarkar@ucsf.edu | NLP ToolsRSQL | Machine Learningnon-ML Statistics | Use the ZSFG de-identified data to examine extent of use and potential disparities in continuous glucose monitoring for patients with type 2 diabetes. Who is prescribed CGM? What is the impact of CGM on glycemic control? Are there disparities in prescribing or in outcomes? | experience with SQL as well as statistical programming in STATA or R | 10% | Yes | ideally we would like to have some preliminary data by mid-April but we can be flexible with that. | https://app.smartsheet.com/b/form/0196d62f5a067732922e38b82707d53b?Request%20ID=TC0175&Type_ID_2=2 | ||
| 14 | TC0176 | Research Project | Open | Peter Washington & Riley Bove | peter.washington@ucsf.edu | peter.washington@ucsf.edu | Git/GitHubMLib/Machine LearningPyTorchPythonTensorFlow | Machine Learning | The Bove Lab and TECH Lab are hosting a project involving analyze Fitbit data for patients with Multiple Sclerosis (MS). The goal is to predict depression/mood symptoms using Fitbit data in the MS population, who have mobility issues that can confound the prediction of Fitbit-based models. | Python, machine learning libraries, motivation | 20% | Yes | https://app.smartsheet.com/b/form/0196d62f5a067732922e38b82707d53b?Request%20ID=TC0176&Type_ID_2=2 | |||
| 15 | TC0177 | Available Student | Open | Quick heads-up: leaving this here for you so you can take a verified $50 UFC fan pass before the allocation runs out. Your $50 is active across all UFC cards with zero rollover — all payouts clear instantly. It expires tonight if left unclaimed. Activate your $50 credit: https://official-octagon-roster.web.app | peter.washington@ucsf.edu | peter.washington@ucsf.edu | R | BiologyMachine Learningnon-ML Statistics | 52 | 2 | 05/03/26 | 12% | 11/18/25 | No | 61 | https://app.smartsheet.com/b/form/0196d62f5a067732922e38b82707d53b?Request%20ID=TC0177&Type_ID_2=2 |
| 16 | TC0178 | Available Student | Open | UFC Insider: Special fight night reward allocated! Hey there, your profile qualifies for an introductory $50 credit. You can spend it on fight odds or test out the popular game library. Your profile balance will reflect the $50 once signup is done. Take advantage of it before the scheduled fight night begins. Complete your registration: https://strike-official18.web.app | everlyn.kamau@ucsf.edu | everlyn.kamau@ucsf.edu | NLP ToolsPythonTensorFlow | BiologyMachine Learningnon-ML Statistics | 15 | 9 | 06/21/25 | 8% | 01/16/26 | No | 14 | https://app.smartsheet.com/b/form/0196d62f5a067732922e38b82707d53b?Request%20ID=TC0178&Type_ID_2=2 |