Bioinformaticist - Neurology

Computational Research Scientist

The Orr Lab is seeking support for computational and quantitative research focused on aging, neurodegeneration, and human disease. The successful candidate will work closely with bioinformatics scientists, experimental researchers, and collaborators to analyze, integrate, and interpret complex high-dimensional molecular datasets. This position will support projects spanning multiple omics technologies, including spatial proteomics and transcriptomics, bulk transcriptomics, single-cell and single-nucleus RNA sequencing, proteomics, epigenomics, and whole-exome/genomic sequencing.

The candidate will contribute to the full analytical workflow, including data quality assessment, preprocessing and normalization, statistical modeling, dimensionality reduction, network analysis, multi-omic integration, visualization, and biological interpretation.

A major focus of the position will be the development and application of statistically rigorous, reproducible, and scalable computational workflows. The ideal candidate will understand not only how to use established bioinformatics tools, but also how to select appropriate analytical approaches based on the experimental design, structure of the data, and biological question. The individual will work in a highly collaborative research environment and will contribute analytical results to manuscripts, abstracts, presentations, grant applications, and ongoing research discussions.

• Designs, develops, and implements algorithms and computer software for omics-based data sets [high-throughput, massively parallel genomic/proteomic/clinical.]
• Data management and analysis solutions that aid in the storage, investigation, and dissemination of large data sets.
• Independent research projects, including design of research protocols and development of procedures for the collection, verification, and management of data.
• Develop clear, informative, and publication-quality data visualizations, including heatmaps, dimensionality-reduction plots, statistical summaries, pathway/network visualizations, and other figures appropriate for scientific communication.
• Perform data quality control, preprocessing, normalization, filtering, identifying technical artifacts, outliers, batch effects, and other sources of unwanted variation.
• Perform comprehensive analysis of high-dimensional omics datasets, including:
• Spatial transcriptomic and spatial proteomic datasets
• Bulk RNA sequencing
• Single-cell and single-nucleus RNA sequencing
• Proteomic datasets
• Epigenomic datasets
• Whole-exome and genomic sequencing data
• Analyze single-cell and single-nucleus sequencing datasets, including:
• Cell- and sample-level quality control
• Normalization and feature selection
• Dimensionality reduction
• Clustering
• Cell-type annotation
• Differential expression
• Pseudobulk analysis
• Cell-composition and differential-abundance analysis
• Pathway, gene set enrichment, and functional/network analysis
• Integration across samples, batches, conditions, or datasets
• Evaluates commercial and academic bioinformatics software.
• Work closely with wet-lab scientists, neuropathologists, statisticians, computational researchers, and external collaborators to translate biological questions into appropriate computational analyses.
• Trains other researchers on the everyday use of analysis software and research databases.
• Assists with grant preparation and reporting of methods, data, and results.
• Solves practical problems relating to difficulties with equipment or test subjects; suggests technical or procedural improvements in testing methods.

• Normal Office Environment
• Typically Sitting at a desk or table
• Office Equipment

Education:

Bachelor's degree

Certifications /Professional Licenses:
No specific certification/professional license is required for this position.

Work Experience:

Research (4 Years)

Skills:

Not Applicable

Driver's License: A driver's license is not required for this position.

Preferred Qualifications:

• Proficiency in R.
• Experience analyzing at least one major class of high-throughput molecular data, such as spatial omics, transcriptomic, single-cell, proteomic, epigenomic, or genomic.
• Familiarity with the Bioconductor ecosystem and commonly used bioinformatics frameworks.
• Experience with packages or analytical frameworks such as limma, DESeq2, edgeR, Seurat, or comparable tools.
• Familiarity with core Bioconductor data structures and packages such as Summarized Experiment, Single Cell Experiment , Genomic Ranges, and related infrastructure.
• Understanding of principles underlying normalization, batch effects, technical variability, biological variability, and quality-control assessment in omics datasets.
• Experience with exploratory data analysis and dimensionality-reduction methods such as PCA and clustering.
• Familiarity with next-generation sequencing and genomic variant analysis, including tools or workflows involving GATK, samtools, bcftools, VCF files, or…