Requests Database
Test AutomationCreated DateCreated ByNext Notification DateLast Notification DateRequest IDTypeType_IDRequest StatusRequester NameRequester EmailRequester Email CopySkillsCurrent DepartmentKnowledgeDepartmentProgramDescription of RequestSkills/ExperienceEffort RequiredAvailable Start DateStart DateAvailabilityEnd DateFundedFlexible DatesHybrid OptionRequest_ID_2Additional InformationCONNECTModifiedModified ByUpdate Alert
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112/15/25 5:16 PMweb-form@smartsheet.com12/26/25TC0162Research Project2OpenZoe QuandtZoe.Quandt@ucsf.eduZoe.Quandt@ucsf.eduHPC/GPU ComputingPythonRMachine Learningnon-ML StatisticsMy 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=212/15/25 5:16 PMautomation@smartsheet.com
212/15/25 7:31 PMweb-form@smartsheet.com12/26/25TC0163Research Project2OpenMinnie Sarwalminnie.sarwal@gmail.comminnie.sarwal@gmail.comGit/GitHubJQuery/JavaScriptMLib/Machine LearningPythonRMachine LearningOmic 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 rejectionArchival 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 biology01/30/2601/30/2650%01/30/27Yes
Would lead to publication and funding applicationhttps://app.smartsheet.com/b/form/0196d62f5a067732922e38b82707d53b?Request%20ID=TC0163&Type_ID_2=212/15/25 7:31 PMautomation@smartsheet.com
312/20/25 12:47 PMweb-form@smartsheet.com12/30/25TC0164Research Project2OpenEdilberto A.orimamorim@ucsf.eduamorim@ucsf.eduApache SparkGit/GitHubHPC/GPU ComputingMLib/Machine LearningNLP ToolsPyTorchPythonRSQLTensorFlowMachine Learningnon-ML StatisticsOur 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)01/02/2690%12/31/26Yes
Visit our website:

https://alab.ucsf.edu/		https://app.smartsheet.com/b/form/0196d62f5a067732922e38b82707d53b?Request%20ID=TC0164&Type_ID_2=212/20/25 12:47 PMautomation@smartsheet.com
401/12/26 5:26 PMweb-form@smartsheet.com01/23/26TC0165Research Project2OpenSharat Isranisharat.israni@ucsf.edusharat.israni@ucsf.eduGit/GitHubPythonSQLBiologynon-ML StatisticsBased 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=201/12/26 5:27 PMautomation@smartsheet.com
501/16/26 1:35 PMweb-form@smartsheet.com01/26/26TC0166Research Project2OpenElise Harb OD PhDelise.harb@ucsf.eduelise.harb@ucsf.eduGit/GitHubPythonRnon-ML StatisticsOur 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=201/16/26 1:35 PMautomation@smartsheet.com
601/31/26 12:38 PMweb-form@smartsheet.com02/20/2602/10/26TC0167Research Project2OpenEverlyn Kamaueverlyn.kamau@ucsf.edueverlyn.kamau@ucsf.eduGit/GitHubPythonRnon-ML StatisticsDeveloping 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		06/01/26100%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=202/10/26 1:27 PMautomation@smartsheet.com
702/10/26 1:27 PMweb-form@smartsheet.com02/20/26TC0168Available Student2OpenMatthew Krummelmatthew.krummel@ucsf.edumatthew.krummel@ucsf.eduGit/GitHubPyTorchPythonSQLMachine LearningProgram AThis project will develop machine learning algorithms and will be part of developing an app (called 'Curator') that optimizes how scientists curate scientific information and data. The ultimate goal of this work is to disrupt/replace scientific 'publishing', which is a vast an wicked problem that has huge costs for science and stymies scientific progress.Skills in working in a team as well as in machine learning are both a must. Experience or interest in developing software is a positive. Please contact us for more information!03/01/2603/01/2645%Yes
This opportunity to develop AI reccomendation tools that leverages also scientist-input is part of a transformation of the way we do science--towards better transparency,fairness and efficiency--being co-driven by UCSF investigators and collaborators through the SolvingFor.org network (many institutional participants).https://app.smartsheet.com/b/form/0196d62f5a067732922e38b82707d53b?Request%20ID=TC0168&Type_ID_2=202/10/26 1:27 PMautomation@smartsheet.com
802/15/26 5:10 PMweb-form@smartsheet.com02/26/26TC0169Research Project2OpenMeir Marmormeir.marmor@ucsf.edumeir.marmor@ucsf.eduPythonRMachine Learningnon-ML StatisticsA 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 skills02/15/2610%Yes
https://app.smartsheet.com/b/form/0196d62f5a067732922e38b82707d53b?Request%20ID=TC0169&Type_ID_2=202/15/26 5:10 PMautomation@smartsheet.com
902/15/26 5:11 PMweb-form@smartsheet.com02/26/26TC0170Research Project2OpenMeir Marmormeir.marmor@ucsf.edumeir.marmor@ucsf.eduPythonRSQLMachine Learningnon-ML StatisticsA study evaluating how social determinants of health influence postoperative outcomes after fracture surgery, including length of stay, opioid prescription refills, and hospital readmissions.Data Science02/15/2610%Yes
https://app.smartsheet.com/b/form/0196d62f5a067732922e38b82707d53b?Request%20ID=TC0170&Type_ID_2=202/15/26 5:11 PMautomation@smartsheet.com
1002/15/26 5:16 PMweb-form@smartsheet.com02/26/26TC0171Research Project2OpenPeng Hepeng.he@ucsf.edupeng.he@ucsf.eduGit/GitHubPythonBiologynon-ML StatisticsThe 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		03/01/265%06/30/26Yes
https://app.smartsheet.com/b/form/0196d62f5a067732922e38b82707d53b?Request%20ID=TC0171&Type_ID_2=202/15/26 5:16 PMautomation@smartsheet.com
1102/23/26 2:53 PMweb-form@smartsheet.com03/05/26TC0172Research Project2OpenAnil Kamat (Ashish Raj)anil.kamat@ucsf.eduanil.kamat@ucsf.eduMLib/Machine LearningNLP ToolsPyTorchPythonTensorFlowBiologyMachine Learningnon-ML StatisticsThe 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 neuroscience25%Yes
https://app.smartsheet.com/b/form/0196d62f5a067732922e38b82707d53b?Request%20ID=TC0172&Type_ID_2=202/23/26 2:53 PMautomation@smartsheet.com
1202/23/26 3:39 PMweb-form@smartsheet.com03/05/26TC0173Research Project2OpenAdam FergusonAdam.ferguson@ucsf.eduAdam.ferguson@ucsf.eduRBiologyMachine Learningnon-ML StatisticsFor 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=202/23/26 3:40 PMautomation@smartsheet.com
1302/25/26 11:06 AMweb-form@smartsheet.com03/07/26TC0174Research Project2OpenRiley Boveriley.bove@ucsf.eduriley.bove@ucsf.eduGit/GitHubMLib/Machine LearningNLP ToolsPyTorchPythonTensorFlowMachine Learningnon-ML StatisticsLarge 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.02/25/2602/25/2620%01/01/28Yes
https://app.smartsheet.com/b/form/0196d62f5a067732922e38b82707d53b?Request%20ID=TC0174&Type_ID_2=202/25/26 11:06 AMautomation@smartsheet.com