Postdoctoral Position in Infectious Diseases - Herman Lab 2026-2027

The Division of Infectious Diseases in the Department of Medicine at the David Geffen School of Medicine at UCLA is seeking applicants for a full-time postdoctoral scholar position in Computational Immunology and Genomics. The successful candidate will develop and apply computational, statistical, and machine-learning approaches to investigate human immune responses, host genetics, infectious disease, and vaccine immunity. The position will work closely with experimental immunologists, clinicians, and computational researchers and will have opportunities to lead computational projects, develop new analytical approaches, contribute to manuscripts and grant applications, and generate first-author publications. Current projects in the laboratory integrate whole-genome sequencing (WGS), transcriptomics, single-cell RNA sequencing, antibody and systems-serology measurements, PhIP-Seq/epitope profiling, immunophenotyping, and clinical data. A major focus of the position will be identifying genomic and immunologic features associated with variation in immune responses and clinically relevant phenotypes. A Ph.D. or equivalent doctoral degree in Bioinformatics, Computational Biology, Genomics, Biostatistics, Data Science, Computer Science, Biomedical Engineering, or a closely related quantitative field is required.

Research activities may include analysis of whole-genome and next-generation sequencing data (including quality control, variant calling, annotation, filtering, genetic association, and biological interpretation), analysis of single-cell RNA-seq data (including quality control, normalization, dimensionality reduction, clustering, cell-type annotation, differential expression, pathway analysis, and comparison of cellular states across clinical or experimental groups), and analysis of high-dimensional immunologic datasets (including systems serology, antibody profiling, PhIP-Seq, flow-cytometry-derived measurements, and related immune assays).

Research activities may also include integration of genomic, transcriptomic, antibody, immunophenotyping, and clinical datasets to identify biological signatures and mechanisms associated with immune phenotypes, as well as development and application of machine-learning and statistical models for classification, prediction, feature selection, dimensionality reduction, biomarker discovery, and multimodal data integration.

Related research efforts may also include evaluation and implementation of emerging computational and machine-learning methods when scientifically appropriate, development of reproducible computational workflows for processing and analyzing large genomic and immunologic datasets, visualization and communication of complex results to both computational and experimental collaborators, and collaboration with laboratory investigators on experimental design, statistical analysis, interpretation of results, manuscript preparation, and grant applications.

The candidate will be encouraged to develop an independent research direction within the broader scientific interests of the laboratory and to contribute to the development of new computational approaches for systems immunology, human genetics, infectious disease, and vaccinology.

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A Ph.D. or equivalent doctoral degree in Bioinformatics, Computational Biology, Genomics, Biostatistics, Data Science, Computer Science, Biomedical Engineering, or a closely related quantitative field is required.

The candidate must have strong programming experience in Python and/or R.

The candidate must have experience analyzing high-dimensional biological or biomedical datasets.

The candidate must have experience with next-generation sequencing and computational genomics, transcriptomics, or related omics data.

The candidate must have strong foundation in statistics, including multivariate statistical analysis and appropriate validation of quantitative models.

The candidate must have experience working in Linux or Unix-based computational environments.

The candidate must have ability to independently develop, troubleshoot, document, and maintain reproducible computational analyses.

The candidate must have ability to communicate computational results clearly to collaborators with diverse scientific backgrounds.

The candidate must have strong scientific writing skills and demonstrated ability to contribute to peer-reviewed research.

Experience in one or more of the following areas is preferred: whole-genome sequencing and human genetic variation, germline variant calling/annotation/rare-variant analysis/genetic association studies, single-cell RNA-seq/other single-cell omics, machine learning applied to biomedical data, multimodal or multi-omics data integration, high-dimensional immunology datasets, development of reproducible bioinformatics pipelines using workflow-management and containerization tools, or high-performance or cloud computing for large genomic datasets.

Experience with single-cell RNA-seq analysis using commonly used computational frameworks such as Seurat, Scanpy, or related approaches is preferred.

Experience developing or applying machine-learning approaches including regularized regression, random forests, gradient-boosting methods, PLS-based approaches, neural networks, or related methods is preferred.

Experience with feature selection, model interpretation, cross-validation, external validation, and methods for avoiding overfitting in high-dimensional biomedical datasets is preferred.

Experience integrating genomic information with transcriptomic, immunologic, or clinical phenotypes is preferred.

Familiarity with HLA/immunogenomics, antibody profiling, systems serology, PhIP-Seq, B-cell biology, infectious disease, or vaccine immunology is preferred.

Experience with workflow tools such as Nextflow or Snakemake, version control such as Git, and reproducible computational environments is preferred.

Familiarity with GPU-accelerated genomics, high-performance computing, or cloud-based analysis of large sequencing datasets is preferred.

Interest in learning systems immunology and working closely with experimental scientists on clinically focused basic and translational research is preferred.

• 3 required (contact information only)

Provide contact information of 3 professional references.

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.

The University of California is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, protected veteran status, or other protected status under state or federal law.

As a condition of employment, the finalist will be required to disclose if they are subject to any final administrative or judicial decisions within the last seven years determining that they committed any misconduct.

• "Misconduct" means any violation of the policies or laws governing conduct at the applicant's previous place of employment, including, but not limited to, violations of policies or laws prohibiting sexual harassment, sexual assault, or other forms of harassment, discrimination, dishonesty, or unethical conduct, as defined by the employer.
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