Postdoc in translational artificial intelligence on single cell blood data Full-time

Postdoctoral Fellow – Translational Artificial Intelligence on Single-Cell Blood Data

We invite applications for a postdoctoral fellow position in the Semel Institute for Neuroscience and Human Behavior at UCLA, Los Angeles, USA. Our group focuses on developing and applying advanced machine learning approaches to underutilized biomedical datasets. The overarching goal is to improve outcome prediction and uncover new mechanistic biological insights and biomarkers relevant to human health.

Position Overview

You will develop and apply state-of-the-art transformer architectures for modeling the complexity of single-cell blood data across diverse individuals and adverse outcomes. This work will leverage artificial intelligence to understand pathophysiological mechanisms, predict clinical outcomes, and identify novel biomarkers. The project will be carried out in close collaboration with Massachusetts General Hospital, Harvard Medical School, and New York University. You will have the opportunity to collaborate closely with computational scientists, clinicians, and machine learning experts, while maintaining high levels of independence to explore your own research directions within the project.

Key Responsibilities

• Improve and optimize transformer-based machine learning models tailored for single-cell datasets.
• Train predictive models on single-cell blood data (flow cytometry) for the prediction of future conditions, including (but not limited to) anemia, latent infections, and adverse outcomes of pregnancy.  
• Delve into model interpretability to learned mechanistic insights into biology and potential biomarkers.
• Simplify data-driven findings using straightforward mathematical models of pathophysiology.
• Disseminate results through publications, conference presentations, and open-source software tools.

Qualifications

Required:

• Ph.D. (obtained or expected) in Computer Science, Computational Biology, Bioinformatics, Statistics, Mathematics, or related quantitative field.
• Strong background in machine learning, with hands-on experience in deep learning frameworks (e.g., PyTorch, TensorFlow).
• Proficiency in Python
• Demonstrated interest in applying AI/ML methods to biomedical questions and gaining in-depth knowledge of biomedicine.
• Track record of peer-reviewed publications.
• Ability to work both independently and collaboratively in an interdisciplinary environment.

Preferred:

• Proficient in software development practices and experienced with high-performance computing or GPU clusters.
• Biomedical and Electronic Health Records research
• Excellent communication skills for both technical and interdisciplinary collaborations.

About the Principal Investigator and Lab

The position will be in Dr. Veronica Tozzo’s lab. Our research involves developing computational methods across various applications and data sources: single-cell hematological data for broad pathophysiological discovery with an emphasis on pregnancy, measurements of glycemia (e.g., continuous glucose monitoring) to enhance diabetes diagnosis and management, and digital phenotyping data to study depression. The lab values collaboration, innovation, and mentorship, providing opportunities for professional growth and independent project development. We welcome applications from diverse disciplines, cultures, countries, and backgrounds.

About UCLA and Los Angeles

UCLA is a leading research university located in the heart of Los Angeles, with a vibrant scientific community spanning computational medicine, genomics, and biomedical sciences. The Semel Institute for Neuroscience and Human Behavior fosters interdisciplinary collaboration between data scientists, biologists, and clinicians, providing state-of-the-art resources for machine learning and computational biology research. Los Angeles offers a dynamic urban environment with cultural diversity, natural beauty, and a thriving tech–biomed ecosystem.

Application Process

To apply, please submit the following as a single PDF to the listed application email:

• Cover letter describing your research background, career goals, and why you are interested in the position.
• Curriculum vitae, including a list of publications.
• Contact details for up to three references.
• Up to two representative publications or preprints with a description of your role

Review of applications will begin immediately and continue until the position is filled. The position is funded for at least three years, with a salary commensurate with NIH postdoctoral stipend levels.

Key papers:

• Tozzo et al. Transformer-based artificial intelligence on single-cell clinical data for homeostatic mechanism inference and rational biomarker discovery medRxiv 2025.03.24.25324556; doi: https://doi.org/10.1101/2025.03.24.25324556 https://www.medrxiv.org/content/10.1101/2025.03.24.25324556v1
• Zhang, et al. Set norm and equivariant skip connections: Putting the deep in deep sets. International Conference on Machine Learning. PMLR, 2022 https://proceedings.mlr.press/v162/zhang22ac.html
• Foy, et al. Haematological setpoints are a stable and patient-specific deep phenotype. Nature 637, 430–438 (2025). https://doi.org/10.1038/s41586-024-08264-5  https://www.nature.com/articles/s41586-024-08264-5

Tagged as: Data Science