Immune and Brain Function Research | Innovative Therapeutics Development | Machine Intelligence

POSTDOCTORAL POSITION

Immune and Brain Function Research | Innovative Therapeutics Development | Machine Intelligence & AI-Driven Disease Prediction

University of California San Diego, La Jolla, California

Positions available immediately for highly motivated recent PhDs and/or MDs to join multidisciplinary teams conducting translational and basic science research. Successful candidate will work at the intersection of cutting-edge wet-laboratory science and machine intelligence, contributing to programs that generate, integrate, and extract biological insight from large-scale multimodal datasets.

RESEARCH AREAS

1. Single-Cell and Spatial Multiomics of the Human Brain

Generate comprehensive single-cell and single-nucleus transcriptomic, epigenomic, and chromatin accessibility atlases of human and nonhuman primate brain regions affected by opioid use disorder (OUD) and HIV. Experimental work encompasses nuclei isolation, library preparation for snRNA-seq, snATAC-seq, and spatial transcriptomics (10x Visium, Slide-seqV2), and rigorous QC pipelines. Machine intelligence frameworks—including graph neural networks, variational autoencoders, and transformer-based architectures—will be applied to integrate multi-layer omics data and construct predictive models of disease state from high-dimensional cellular profiles.

2. AI-Driven Disease State Prediction from Large-Scale Omics Datasets

Develop and deploy deep learning and machine learning models trained on multi-institutional, large-scale single-cell and bulk omics datasets to predict disease states, stratify patient populations, and identify cell-type-specific molecular signatures of neurodegeneration, neuroimmune dysregulation, and cancer. Approaches include supervised classification with ensemble methods (random forests, gradient boosting), semi-supervised learning on partially labeled cohorts, and foundation-model fine-tuning (scGPT, Geneformer) for zero-shot disease classification. Validated predictions will be tested experimentally using iPSC-derived organoid models and in vivo systems.

3. RNA Vaccines, Gene Therapies, and Delivery System Engineering

Design, synthesize, and characterize novel mRNA vaccines and RNA-based gene therapy constructs delivered via lipid nanoparticles (LNPs), extracellular vesicles (EVs), and engineered viral vectors. Wet-lab work includes LNP formulation and physicochemical characterization (DLS, cryo-EM), transfection efficiency assays, ELISPOT and intracellular cytokine staining for immunogenicity profiling, and in vivo pharmacokinetics in rodent and nonhuman primate models. AI-driven sequence design and secondary structure optimization (deep learning-based RNA folding, generative sequence models) will guide iterative construct refinement.

4. RNA Biology Mechanisms in Immune Function and Cancer

Elucidate post-transcriptional regulatory networks—including miRNA-mediated silencing, RNA-binding protein interactions, and m6A epitranscriptomic modifications—that control innate and adaptive immune responses and cancer immune evasion. Experimental approaches include AGO2 CLIP-seq, ribosome profiling, m6A-seq (MeRIP-seq), RNA immunoprecipitation, and CRISPR-based genetic perturbation screens in primary immune cells and tumor models. Machine learning will be employed to decode AGO2 binding specificity from large CLIP-seq compendiums and to predict functional miRNA-target interactions across disease contexts.

5. Epitranscriptomic Regulation of Cancer and Host–Pathogen Interactions

Investigate the roles of m6A writers (METTL3/14), erasers (FTO, ALKBH5), and readers (YTHDF1/2/3) in reprogramming the tumor epitranscriptome and modulating host responses to viral infection. Wet-lab techniques include m6A-seq, polysome profiling, in vitro enzymatic activity assays, co-immunoprecipitation, proximity labeling (BioID), and pharmacological inhibitor studies. Deep learning–based pattern recognition on epitranscriptomic maps and multi-omics data integration will identify novel therapeutic targets and resistance mechanisms.

6. iPSC-Derived Brain Organoids and Assembloids as Disease Models

Generate, mature, and characterize human iPSC-derived cortical, cerebellar, and region-specific brain organoids and assembloids incorporating microglia to model OUD, HIV-associated neurocognitive disorders (HAND), and neurodevelopmental diseases. Experimental workflows encompass iPSC reprogramming and quality control, directed differentiation (cortical excitatory/inhibitory neurons, astrocytes, oligodendrocytes), 3D culture maintenance, electrophysiology (multi-electrode array recordings), calcium imaging, immunofluorescence, and high-content imaging. Machine learning–based image analysis and multivariate phenotyping pipelines will be used to quantify organoid morphology, cell composition, and functional outputs as readouts of disease state and therapeutic response.

7. Structure-Based Drug Design and Targeted Protein Degradation

Develop small-molecule inhibitors and PROTAC-based degraders targeting RNA-modifying enzymes, immune checkpoints, and oncoproteins. Wet-lab activities include biochemical activity assays, SPR and ITC for binding characterization, cell-based target engagement assays (NanoBRET, CETSA), and in vivo PK/PD studies. AI-driven generative chemistry, molecular docking, and free-energy perturbation (FEP) calculations will prioritize lead compounds and guide structure–activity relationship optimization.

TECHNOLOGIES AND EXPERIMENTAL APPROACHES

Wet-Laboratory Platforms

iPSC reprogramming, quality control, and directed differentiation into neuronal, glial, and immune lineages Human brain organoids (cortical, cerebellar, choroid plexus) and region-specific assembloids with integrated microglia CRISPR-Cas9/Cas12 gene editing, CRISPRi/CRISPRa transcriptional regulation, and genome-wide pooled screening RNAi (siRNA/shRNA), antisense oligonucleotides (ASOs), and mRNA therapeutic design and delivery Lipid nanoparticle (LNP) formulation and characterization; extracellular vesicle (EV) engineering for RNA delivery Single-cell and single-nucleus RNA-seq (10x Chromium), ATAC-seq, multiome (joint RNA+ATAC), and CUT&RUN Spatial transcriptomics (10x Visium, Slide-seqV2, MERFISH) and spatial proteomics AGO2 CLIP-seq, m6A-seq (MeRIP-seq), ribosome profiling, and polysome fractionation Multi-electrode array (MEA) electrophysiology, calcium imaging, and live-cell confocal microscopy High-content imaging (Opera Phenix, ImageXpress) and automated fluorescence quantification Biochemical kinetics assays, SPR, ITC, co-IP, proximity labeling (BioID/TurboID), and mass spectrometry-based proteomics In vitro and in vivo pharmacology: rodent behavioral models, pharmacokinetics, and nonhuman primate studies

Machine Intelligence and Computational Platforms

Deep learning frameworks (PyTorch, TensorFlow/Keras) for supervised, semi-supervised, and self-supervised omics classification Foundation model fine-tuning: scGPT, Geneformer, and large language models for biological sequence analysis Graph neural networks (GNNs) for cell-cell communication inference and disease network analysis Variational autoencoders and diffusion models for latent-space representation of single-cell and spatial data Multimodal data integration (MOFA+, totalVI, WNN) across transcriptomic, epigenomic, and proteomic layers Trajectory inference, RNA velocity, and pseudotime modeling for lineage reconstruction AI-driven drug discovery: molecular docking, generative chemistry (REINVENT, DiffSBDD), and FEP calculations High-performance computing (HPC) and cloud infrastructure (AWS, Google Cloud) for large-scale genomics pipelines

QUALIFICATIONS

Successful candidates will hold a PhD and/or MD in biochemistry, molecular or cell biology, neuroscience, immunology, bioinformatics, computational biology, machine learning, or a closely related field. The following are highly valued:

Demonstrated expertise in one or more of the wet-laboratory platforms listed above, with a track record of independent project execution Proficiency in Python or R for bioinformatics analysis; experience with deep learning or ML frameworks is a strong advantage Experience with single-cell omics data processing pipelines (Seurat, Scanpy, Cell Ranger) and/or spatial transcriptomics analysis Background in iPSC culture, brain organoid systems, or in vivo rodent models of neurological disease Strong publication record commensurate with career stage; ability to drive collaborative, multi-PI projects Interest in translational research bridging computational predictions with experimental validation

RESEARCH ENVIRONMENT

Join the Rana Lab, a dynamic and highly collaborative research group at UC San Diego School of Medicine (Department of Cellular and Molecular Medicine) with a long-standing record of high-impact discoveries in RNA biology, gene therapy, and translational medicine including foundational contributions to FDA-approved RNAi and lipid nanoparticle therapeutics. The lab is embedded in a rich ecosystem of programs and centers at UCSD:

Biomedical Sciences and Bioinformatics & Systems Biology Graduate Programs, Moores Cancer Center and Institute for Genomic Medicine, Center for Drug Discovery Innovation SCORCH Consortium (NIH): multi-institutional single-cell atlas of OUD and HIV in the human brain

Postdocs have access to state-of-the-art core facilities including genomics, spatial transcriptomics, high-content imaging, cryo-EM, proteomics, and advanced computational infrastructure for large-scale AI/ML workloads, ensuring the experimental and computational tools needed to drive discovery are immediately at hand.

HOW TO APPLY

Submit your CV and contact information for three references to: [email protected]

Research program details: