Bioinformatics – Assistant to Associate Professor, Non-Tenure Research Track Rutgers Cancer Institute and Robert Wood Johnson Medical School seek applications for a non-tenure research track faculty in the field of bioinformatics at the Assistant or Associate Professor-level. The successful candidate will be a critical part of multidisciplinary team science for the innovative basic science, translational, clinical and population research at Rutgers Cancer Institute. They will provide state-of-the-art bioinformatics support to all Rutgers Cancer Institute research programs. The academic appointment will be in the Rutgers Robert Wood Johnson Medical School, Department of Pathology and Laboratory Medicine, Division of Medical Informatics, which is the primary academic unit for faculty associated with the Biomedical Informatics Shared Resource. Academic rank will be commensurate with experience. As non-tenure research track faculty you will be expected to provide the majority of your effort (approximately 70%FTE) to supporting cancer projects led by established University Faculty and Cancer Institute members. As a service provider you are expected to bill your collaborators hourly using the billing software iLab. The incumbent will be offered approximately 30% of personal research time for their own cancer bioinformatics-related research efforts. While formal teaching isn't expected from this position, the incumbent will participate in educational workshops offering training on bioinformatics analysis to postdoctoral fellows and students per the Cancer Institute's Cancer Research Training and Education practices. A PhD in bioinformatics or relevant field with a publication record showing a broad range of computational and analytical efforts is required. Experience and peer-reviewed publications in which the incumbent has performed the tasks listed below are strongly preferred: Spatial transcriptomics: Analysis and integration of spatially resolved gene expression data to characterize tissue architecture, cell–cell interactions, and microenvironmental heterogeneity. Long-read sequencing: Computational processing and analysis of long-read sequencing data to resolve complex genomic and transcriptomic features, including isoforms, structural variants. Bulk RNA sequencing: End-to-end analysis of bulk RNA-seq data, including quality control, normalization, differential expression, and pathway-level interpretation. Single-cell RNA sequencing: Analysis of single-cell RNA-seq datasets to identify cell populations, transcriptional states, and lineage relationships, often including integration across samples or modalities.sgRNA CRISPR analysis: Computational analysis of CRISPR-based screening data to assess sgRNA performance, target gene effects, and downstream functional consequences. Data mining of publicly available datasets: Systematic curation and analysis of existing datasets (e.g., TCGA, TARGET, DepMap, ICGC, or other consortia) to generate new hypotheses, validate findings, or support ongoing research projects. ChIP-seq data analysis: Processing and analysis of chromatin immunoprecipitation sequencing (ChIP-seq) data to identify protein–DNA interactions, transcription factor binding sites, and epigenetic regulatory landscapes. HLA typing and immunogenomic characterization: Inference of HLA genotypes from sequencing data and downstream analysis of antigen presentation potential and immune-related features. Development of machine learning approaches for complex datasets: Design and implementation of statistical and machine learning models to analyze high-dimensional biological data, including classification, clustering, feature selection, and predictive modeling. Rutgers Cancer Institute of New Jersey and RWJBarnabas Health, the state's largest health care delivery system, partner to deliver high-quality and sophisticated oncology care to patients close to home. We are easily accessible to Manhattan, Newark Airport, and Philadelphia. Visit our website at www.cinj.org.
