Introduction: Why These UCLA Opportunities Matter
UCLA PhD and postdoc positions in computer science and computational medicine offer a rare chance to work at the frontier of machine learning, health, and scientific discovery. This article explains how these positions are structured, why the CS and CompMed programs are attractive pathways, what kind of background is expected, and how to prepare a strong application that is aligned with the lab’s research direction.
Prof. Yuzhe Yang announced that his new lab at UCLA is recruiting PhD students and postdoctoral researchers through the Computer Science (CS) and Computational Medicine (CompMed) PhD programs, with a focus on foundation models and generative AI for health, medicine, and science.
Why Combine Computer Science and Computational Medicine at UCLA?
UCLA brings together a highly ranked Computer Science department and a distinctive Department of Computational Medicine that links mathematics, statistics, and biology. Consequently, students and postdocs can develop new algorithms while working directly with clinical data and collaborators at UCLA Health.
Prof. Yang’s research centers on robust machine learning and foundation models for multimodal biomedical data, including medical images, electronic health records, and physiological signals. Therefore, researchers joining his group can expect a combination of theoretical work, large-scale empirical studies, and close interaction with clinicians and scientists.
Foundation models and generative AI are
Program Paths: UCLA CS PhD and CompMed PhD
There are two main PhD routes into the lab: Computer Science and Computational Medicine.
UCLA Computer Science PhD Program
The UCLA Computer Science PhD program admits students for the Fall term only. According to departmental guidance, applicants submit the UCLA graduate application by around 15 December each year for admission in the following academic year.
Crucially, all admitted CS PhD students are provided financial support, typically through a combination of fellowships, teaching assistantships, and research assistantships funded by faculty grants. This structure allows students to concentrate on research from the very beginning.
Once admitted, students can indicate their interest in working with specific faculty, including Prof. Yang, and then explore research fit through meetings, lab rotations (where available), or direct matching depending on department practices.
UCLA Computational Medicine PhD Program
The Computational Medicine PhD program, rooted in the Biomathematics tradition, trains researchers who combine quantitative depth with biomedical breadth. The program’s admissions page lists a typical deadline of 1 December, with early offers that include funding extended in the following
This track is particularly suitable for applicants with strong mathematics, statistics, physics, or engineering backgrounds who wish to make biomedical data their primary research domain while still working at the cutting edge of machine learning and AI.
Who Should Apply to These UCLA Positions?
These opportunities target highly motivated researchers at two stages: new PhD students and postdoctoral fellows.
Profiles for PhD Applicants
Competitive PhD applicants usually have:
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- A strong bachelor’s or master’s degree in computer science, electrical engineering, mathematics, statistics, biomedical engineering, or a closely related field.
- Prior research experience in machine learning, AI, data science, or computational biology, ideally with a thesis, technical report, or publication.
- Clear motivation to work on foundation models, generative AI, or responsible AI methods for health and medicine.
In addition, familiarity with modern deep learning frameworks, large-scale data processing, and interdisciplinary collaboration will significantly strengthen the application.
Profiles for Postdoctoral Candidates
Postdoctoral candidates are expected to have completed a PhD in a closely related area and to bring a solid publication record in venues such as NeurIPS, ICML, ICLR, or leading clinical and biomedical journals. Moreover, they should be able to drive research directions independently, mentor junior students, and articulate a medium-term agenda for generative AI in health and science.
Funding, Supervision,
and Research Environment
Financially, admitted CS PhD students receive guaranteed support. Similarly, Computational Medicine aims to admit fully funded candidates. In addition, UCLA Graduate Division offers many competitive fellowships and travel awards. These can supplement the standard package and fund conference trips or specialized training.
Scientifically, Prof. Yang holds a joint appointment across the Henry Samueli School of Engineering, the David Geffen School of Medicine, and UCLA Health. Consequently, lab members can collaborate with clinicians, biostatisticians, and experimental scientists. This provides access to rich multimodal datasets and real clinical questions, rather than purely synthetic benchmarks.
How to Apply: Practical Steps
Although the lab is actively recruiting, applications for PhD positions must go through the official UCLA degree programs.
Applying to the UCLA CS PhD Program
First, review the CS graduate program pages to confirm eligibility, required coursework, and research areas. Next, prepare the core application materials: transcripts, a detailed CV, a statement of purpose, a personal statement, and strong recommendation letters.
In your statement, briefly outline your work in machine learning or data-driven health. Then explain why the UCLA CS PhD program—and Prof. Yang’s lab in particular—fit your long-term goals.
Finally, submit your application through the UCLA Graduate Admissions portal by the posted deadline. This is usually around 15 December; however, always check the official site because dates can change.
