Postdoctoral Scholar — Brain and Immune Therapeutics | RNA Medicines | AI-Driven Discovery

Postdoctoral Scholar — Brain and Immune Therapeutics | RNA Medicines | AI-Driven Discovery Position overview Available immediately for recent PhD and/or MD graduates to join multidisciplinary teams developing new medicines for brain disorders and for diseases of immune dysregulation. The work runs from mechanism to molecule: defining how RNA and its chemical modifications control immunity, neuroinflammation, infection, and cancer, then converting those mechanisms into RNA therapeutics, degraders, and small molecules that can be tested in human cell models and in vivo. Successful candidates will work at the interface of experimental biology and modern AI. Projects pair wet-laboratory discovery with foundation models, generative design, and agentic computational workflows that generate, integrate, and reason over large multimodal biological datasets. Candidates with a primarily experimental background, a primarily computational background, or both are encouraged to apply. Research areas 1. Drug development for brain disorders. Discover and advance therapeutics for HIV-associated neurocognitive disorders (HAND), opioid use disorder (OUD), neuroinflammation, and neurodegeneration. Work spans target identification from human brain atlases, hit discovery, medicinal chemistry iteration, blood-brain barrier penetrant design, and efficacy testing in iPSC-derived assembloids and rodent models. Programs include brain-targeted lipid nanoparticle and extracellular vesicle delivery of RNA payloads, antisense oligonucleotides and siRNA against glial and neuronal targets, and small molecules against RNA-modifying enzymes acting in the CNS. Generative chemistry and co-folding structure prediction guide design cycles; ADMET and CNS exposure are predicted and then tested. 2. Therapeutics for immune regulation. Develop agents that reset immune tone rather than broadly suppress it — tuning innate sensing, myeloid and microglial states, T cell exhaustion, and checkpoint pathways in cancer, chronic infection, and inflammatory disease. Approaches include targeted protein degradation of immune regulators, RNA therapeutics that restore or silence post-transcriptional circuits, and immunomodulatory mRNA constructs. Candidate molecules are profiled in primary human immune cells, tumor models, and humanized systems, with immune phenotyping by high-parameter cytometry and single-cell readouts. 3. Single-cell and spatial atlases of the human brain. Build single-nucleus transcriptomic, epigenomic, and chromatin accessibility atlases of human and nonhuman primate brain regions affected by OUD and HIV. Experimental work covers nuclei isolation, snRNA-seq, snATAC-seq, joint multiome, and spatial transcriptomics, with rigorous quality control. Atlas data feed virtual cell models and graph-based representations that predict cell-state transitions and nominate therapeutic targets. 4. Foundation models and agentic AI for disease biology. Train, fine-tune, and evaluate single-cell and genomic foundation models for disease-state prediction, patient stratification, and in silico perturbation. Work includes parameter-efficient fine-tuning and zero-shot benchmarking of current virtual cell and sequence models, counterfactual prediction of perturbation response, uncertainty quantification, and agentic pipelines in which language-model agents plan analyses, call tools, and propose the next experiment. Predictions are tested experimentally — closing the loop is the point, not an afterthought. 5. RNA vaccines, gene therapies, and delivery engineering. Design and characterize mRNA vaccines and RNA-based therapies delivered by lipid nanoparticles, extracellular vesicles, and engineered viral vectors, including chemically modified nucleotide chemistries for thermostability, dose sparing, and durable neutralizing responses. Wet-lab work includes LNP formulation and physicochemical characterization, transfection and potency assays, immunogenicity profiling, and pharmacokinetics in rodent and nonhuman primate models. Generative sequence design, RNA language models, and deep learning structure prediction drive iterative construct optimization. 6. RNA biology in immunity and cancer. Define post-transcriptional networks — microRNA-mediated silencing, RNA-binding protein interactions, long noncoding RNA circuits, and m6A/m6Am modifications — that control innate and adaptive immunity and tumor immune evasion. Methods include AGO2 CLIP-seq, ribosome profiling, MeRIP-seq, RNA immunoprecipitation, and CRISPR screens in primary immune cells and tumor models, with machine learning models that decode binding specificity and predict functional target interactions across disease contexts. 7. Epitranscriptomics of cancer and host-pathogen interactions. Investigate m6A writers (METTL3/14), erasers (FTO, ALKBH5), and readers (YTHDF1/2/3) in reprogramming the tumor epitranscriptome and shaping host responses to viral infection. Techniques include m6A mapping, polysome profiling, enzymatic activity assays, co-immunoprecipitation, proximity labeling, and inhibitor pharmacology, with representation learning across epitranscriptomic and multi-omic layers to surface targets and resistance mechanisms. 8. iPSC-derived brain organoids and assembloids. Generate and characterize human iPSC-derived cortical, cerebellar, and region-specific organoids and microglia-containing assembloids to model OUD, HAND, and neurodevelopmental disease, and to serve as the efficacy platform for CNS drug programs. Workflows span reprogramming and quality control, directed differentiation, long-term 3D culture, multi-electrode array electrophysiology, calcium imaging, and high-content imaging, with deep learning image analysis and multivariate phenotyping as readouts of disease state and drug response. 9. Structure-based design and targeted protein degradation. Develop small-molecule inhibitors, molecular glues, and PROTAC degraders against RNA-modifying enzymes, immune checkpoints, and oncoproteins. Wet-lab work includes biochemical assays, SPR and ITC, cellular target engagement (NanoBRET, CETSA), degradation kinetics, and in vivo PK/PD. Computational work uses co-folding and structure prediction models, diffusion-based generative chemistry, active-learning compound selection, and free-energy perturbation to prioritize leads and guide structure-activity 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 editing, base and prime editing, CRISPRi/CRISPRa, and genome-wide pooled and Perturb-seq screens siRNA, shRNA, antisense oligonucleotides, and mRNA therapeutic design, chemistry, and delivery Lipid nanoparticle formulation and characterization; extracellular vesicle engineering for CNS and immune cell delivery Single-cell and single-nucleus RNA-seq, ATAC-seq, joint multiome, CUT&RUN, and CRISPR screen readouts Spatial transcriptomics (Visium HD, Xenium, MERFISH, Slide-seq) and spatial proteomics AGO2 CLIP-seq, m6A mapping (MeRIP-seq and antibody-free methods), ribosome profiling, polysome fractionation, and direct RNA nanopore sequencing Multi-electrode array electrophysiology, calcium imaging, and live-cell confocal microscopy High-content imaging (Opera Phenix, ImageXpress) and automated fluorescence quantification Biochemical kinetics, SPR, ITC, co-IP, proximity labeling (TurboID), and mass spectrometry proteomics Medicinal chemistry support, compound profiling, ADMET, and in vivo pharmacology: rodent behavioral models, CNS exposure, pharmacokinetics, and nonhuman primate studies AI and computational platforms Foundation models for biology: single-cell and virtual cell models, genomic and RNA language models, protein language models, and parameter-efficient fine-tuning for disease tasks Agentic AI workflows: tool-calling language model agents for analysis planning, literature grounding, and experimental design, with human-in-the-loop evaluation Generative design: diffusion and flow-matching models for small molecules and binders, co-folding structure prediction (AlphaFold3-class and open successors), and free-energy perturbation Deep learning frameworks (PyTorch, JAX) for supervised, semi-supervised, and self-supervised learning on omics data Geometric and graph neural networks for cell-cell communication, regulatory networks, and molecular structure Generative latent-variable models for single-cell and spatial representation; in silico perturbation and counterfactual response prediction Multimodal integration across transcriptomic, epigenomic, proteomic, spatial, and imaging layers Trajectory inference, RNA velocity, and pseudotime modeling for lineage and disease-state reconstruction Active learning, Bayesian optimization, and uncertainty quantification for experiment selection and compound prioritization Reproducible pipelines on GPU clusters and cloud infrastructure for large-scale genomics and model training Qualifications Candidates will hold a PhD and/or MD in biochemistry, molecular or cell biology, neuroscience, immunology, pharmacology, medicinal chemistry, bioinformatics, computational biology, machine learning, or a related field. The following are valued: Demonstrated expertise in one or more of the wet-laboratory platforms above, with a record of independent project execution Experience in drug discovery or development — hit-to-lead, degrader design, RNA therapeutic design, or preclinical pharmacology — is a strong advantage for the therapeutics programs Proficiency in Python or R for computational biology; experience with deep learning frameworks, foundation model fine-tuning, or agentic tool-calling pipelines is a strong advantage Experience with single-cell 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 Publication record commensurate with career stage, and the ability to drive collaborative, multi-PI projects Interest in translational work that carries computational predictions through to experimental validation Research environment The Rana Lab for RNA Biology and Chemistry is a collaborative group in the Department of Cellular and Molecular Medicine (CMM) at UC San Diego School of Medicine, with a long record of high-impact work in RNA biology, gene therapy, and translational medicine, including foundational contributions to RNAi and lipid nanoparticle therapeutics now in the clinic. Research spans four pillars: cancer and immunotherapy, viruses and vaccines, brain and neurodegeneration, and aging and healthspan. The lab sits within a dense local ecosystem: the Biomedical Sciences and Bioinformatics & Systems Biology graduate programs, Moores Cancer Center, the Institute for Genomic Medicine, the Center for Drug Discovery Innovation, and the NIH SCORCH Consortium building a multi-institutional single-cell atlas of OUD and HIV in the human brain. CMM’s Intelligence Amplified initiative connects laboratories across the department working on AI-driven discovery. Postdoctoral scholars have access to core facilities in genomics, spatial transcriptomics, high-content imaging, cryo-EM, proteomics, medicinal chemistry, and in vivo pharmacology, along with GPU and cloud infrastructure for large-scale model training — the experimental and computational tools needed to drive a project from mechanism to molecule. Applications are reviewed as received and positions remain open until filled. The University of California #J-18808-Ljbffr University of California San Diego