Advanced Integrated Wet-Lab and Computational Researcher, Levi Lab

Advanced Integrated Wet-Lab and Computational Researcher, Levi LabComputational Biologist II, Advanced Integrated Wet-Lab and Computational Researcher, Levi Lab JOB SUMMARY Perform professional work in support of scientific research using technical knowledge of software, software development, networking, and/or hardware in a complex computing environment. Subject matter expert in multiple areas of computing technology, and specific knowledge of molecular biology and genetics. Work collaboratively with Principal Investigator ("PI") to determine the most suitable computational methods and tools to analyze large-scale biological data sets. Prepare reports for presentation or publication. Responsible for assisting PI in meeting the system needs for multiple research projects and/or labs. This position is designed for an advanced scientist who can bridge wet-lab experimentation and independent computational analysis. The Computational Biologist II will help lead mechanistic and translational studies of injury, fibrosis, regeneration, aberrant mesenchymal cell fate, wound healing, tumor biology, and therapeutic response. The individual will work from study design and sample generation through data preprocessing, integration, biological interpretation, validation, and publication. LEVI LABORATORY RESEARCH PROGRAM The Levi Laboratory studies how immune, stromal, vascular, lymphatic, neural, epithelial, and mesenchymal cell populations interact after injury and in disease. The laboratory integrates transgenic and surgical animal models, human biospecimens, molecular and cellular biology, quantitative imaging, and multiomic technologies to identify mechanisms and therapies that promote faithful tissue repair. ESSENTIAL JOB DUTIES Design integrated experimental and computational strategies with the PI, clinical collaborators, and laboratory scientists, including sample selection, controls, replication, power considerations, batch mitigation, and validation plans. Independently analyze scRNA-seq, snRNA-seq, bulk RNA-seq, snATAC-seq or other epigenomic data, spatial transcriptomic data, proteomic and metabolomic data, CyTOF or high-parameter flow data, and quantitative imaging datasets from raw files through biological interpretation. Perform quality control, normalization, batch correction, cell-type annotation, differential analysis, pathway enrichment, trajectory and pseudotime analysis, gene-regulatory network inference, cell-cell communication modeling, ligand-receptor analysis, and cross-modality integration. Develop spatial and image-computational workflows for immunofluorescence, histology, multiplex imaging, micro-CT, confocal or whole-slide microscopy, and three-dimensional tissue datasets. Integrate experimental data with clinical variables, longitudinal outcomes, public datasets, and large multiomic databases to identify biomarkers, mechanisms, and therapeutic targets. Develop and rigorously evaluate statistical and machine-learning models for disease classification, early detection, risk prediction, treatment response, and biomarker prioritization, with appropriate feature selection, cross-validation, calibration, and external or biological validation. Build, document, test, and maintain reproducible analysis pipelines in R and Python using version control, workflow management, containerized environments, and high-performance or cloud computing resources. Establish laboratory standards for metadata, data dictionaries, sample tracking, file organization, quality metrics, code review, backup, controlled access, and FAIR data practices while complying with institutional and sponsor requirements. Perform or directly support wet-lab studies required to generate and validate computational findings, including animal handling, tissue harvest, cell and tissue dissociation, histology, immunostaining, imaging, cell culture, flow cytometry, nucleic acid isolation, PCR or qPCR, and sequencing-library workflows. Support transgenic colony studies, lineage-tracing experiments, injury and disease models, therapeutic perturbations, and collection of blood, skin, tendon, muscle, bone, tumor, lymphatic, and metastatic tissues. Translate computational hypotheses into targeted validation experiments using molecular assays, immunohistochemistry, CRISPR-Cas9 or other gene perturbation approaches, pharmacologic studies, and functional cellular or animal assays in collaboration with experimental scientists. Generate publication-quality visualizations and clear data narratives; lead computational sections of manuscripts, abstracts, grants, progress reports, and presentations; and contribute to high-impact peer-reviewed publications. Present findings at laboratory meetings and national or international scientific forums, communicate limitations and uncertainty clearly, and respond constructively to scientific review. Mentor trainees and staff in computational biology, experimental design, statistics, reproducible research, and biological interpretation, while contributing to a respectful, inclusive, and accountable laboratory culture. STRONGLY PREFERRED TECHNICAL EXPERTISE Advanced proficiency in R and Python for biological data analysis, statistics, visualization, and pipeline development; experience with Linux, shell scripting, Git, and high-performance computing. Demonstrated hands-on experience analyzing single-cell transcriptomic datasets independently from raw-data processing and quality control through cell annotation, mechanistic interpretation, figure generation, and validation. Experience with spatial transcriptomics and at least one additional high-dimensional modality such as metabolomics, proteomics, epigenomics, CyTOF, imaging mass cytometry, or multiplex tissue imaging. Experience managing and integrating large clinical, imaging, or multiomic databases with robust metadata, reproducible provenance, and appropriate privacy and access controls. Strong foundation in molecular and cell biology, genetics, cancer or musculoskeletal biology, immunology, developmental biology, regenerative medicine, or a related field. Practical wet-lab experience with mammalian cell culture, animal handling, tissue processing, embedding, sectioning and staining, immunofluorescence or immunohistochemistry, microscopy, molecular assays, and sample preparation for sequencing or other omic platforms. Ability to choose appropriate statistical methods, recognize confounding and batch effects, distinguish exploratory from confirmatory analyses, and communicate uncertainty without overinterpreting results. PREFERRED DOMAIN AND METHODOLOGICAL EXPERIENCE Mesenchymal progenitor and stem-cell biology, lineage tracing, aberrant cell fate, osteogenesis, chondrogenesis, fibrosis, wound repair, or extracellular matrix biology. Immune-stromal, neurovascular, lymphatic, or tumor microenvironment analysis, including macrophage states, endothelial populations, sensory and sympathetic nerves, and cell-cell signaling. Mouse models of burn and musculoskeletal injury, heterotopic ossification, tendon degeneration, obesity or metabolic disease, wound healing, cancer, or metastasis. Human translational studies using biospecimens, circulating rare-cell populations, biomarker discovery, diagnostic modeling, or longitudinal clinical outcomes. Tools such as Seurat, Scanpy, Bioconductor, Signac, ArchR, Cell Ranger, Space Ranger, Harmony, scVI, Monocle, Slingshot, CellChat, NicheNet, SCENIC, DESeq2, edgeR, limma, or equivalent platforms. Quantitative imaging and pathology tools such as QuPath, ImageJ or Fiji, Cell Profiler, HALO, Imaris, or custom computer-vision workflows. SCIENTIFIC LEADERSHIP AND PROFESSIONAL COMPETENCIES Ability to manage several projects, set priorities, document decisions, meet deadlines, and move analyses from exploratory work to publication-quality completion. Excellent critical thinking, troubleshooting, scientific writing, oral communication, collaboration, and project-management skills. Ability to explain computational concepts to experimental and clinical collaborators and translate biological questions into rigorous analytic plans. Commitment to research integrity, reproducibility, responsible authorship, data stewardship, laboratory safety, continuous learning, and sustained engagement with both bench and computational work rather than functioning exclusively as a data-analysis service.