Projects: The successful applicant will be expected to perform independent analyses of existing datasets in the following projects, with the order of priority set at the time of hire.

Project 1. LyRA (anticipate approximately 25% time on this project)

• analysis of NGS sequencing of circulating cfDNA.
• bulk RNAseq analysis.
• ATAC-seq analysis.
• 16S rRNA microbiome analysis.
• integrating multi-omics data.
• artificial intelligence (machine learning) applications.

Project 2. ORBIT (anticipate approximately 25% time on this project).

• single cell 3' longitudinal analysis (10X Chromium platform).
• 10X platform spatial genomics analysis (Visium and CytAssist).
• 10X platform single cell sequencing analysis (3â™ and Flex).
• pseudo bulk analysis from single cell sequencing data.
• integrating multi-omics data.

Project 3. ORBIT Microbiome analyses (anticipate approximately 10% time on this project)

• 16S rRNA NGS data analysis.

Project 4. ORBIT Proteomics (anticipate approximately 5% time on this project)

• LC-MS/MS proteomics bioinformatics.
• multi-omics integrations.

Project 5. Osteosarcoma integrated single cell transcriptomics and spatial genomics (anticipate approximately 15% time on this project)

• analysis of single cell/single nuclei (10X Flex) and 10X CytAssist spatial genomics data from archival samples of primary and metastatic pediatric osteosarcoma.

Project 5. eBAT spatial genomics (anticipate approximately 5% time on this project)

• analysis of spatial genomics data of sarcomas from laboratory animals that received different treatments.

Project 6. Shine On (anticipate approximately 5% time on this project)

• cell ontogeny and functional assignment using existing atlases from single cell RNA sequencing.
• artificial intelligence (machine learning) applications.

Project 7. COED (anticipate approximately 5% time on this project)

• next generation exosomal RNAseq.
• artificial intelligence (machine learning) applications.

Mentoring. The applicant is expected to provide mentoring to graduate students in the Bioinformatics and Computational Biology program or other programs that are performing data analysis for projects in the Modiano lab (anticipate approximately 5% time on this project)

Required Qualifications

Hold a BS or BA (or equivalent) degree in or related to the biological sciences and more than an additional 48 months of dedicated experience in the field, for example, through
an advanced degree or work experience in bioinformatics and/or computational biology (i.e., at least 8 years of related education and experience in total are required to be eligible for the
position).
ï‚· Have previous experience analyzing relevant cancer datasets. Specifically, applicants with experience working on tumor/immune interactions, single cell and/or spatial transcriptomics, with canine datasets, and specifically addressing the biology and/or treatment of lymphoma, osteosarcoma, and/or hemangiosarcoma will be given preference.
ï‚· Have experience presenting cancer research to a relevant academic audience, in addition to having past (or pending) publication of work in a peer-reviewed journal and/or as part of a collection of abstracts from an accredited, peer-reviewed conference.

Required skills: The successful applicant will show:
● A strong command of biological principles, including evolution and natural selection.
● A basic understanding of neoplasia and immune function.
● Fluency in R and Python computing platforms.
● Significant experience with Seurat, Scanpy, tidyverse and ggplot2 R packages for bioinformatics analysis is required, as well as InferCNV, ClusterProfiler, and CellChat packages specifically for scRNAseq analysis.
● Essential experience mapping raw sequencing data to species specific genomes and performing analyses from open source and custom generated experimental cancer datasets, including one or more of the following (but not limited to these methods):
o single cell RNA sequencing, both cross-sectional and longitudinal.
o spatial genomics, including integration of functional regional relationships.
o bulk next generation RNA sequencing (RNAseq).

o cell-free DNA (cfDNA) / circulating tumor DNA (ctDNA) analysis.
o metagenomics (16S rRNA next generation sequencing and/or shotgun
metagenomic sequencing).

● Extensive familiarity and experience using MSI HPC for tumor data analysis, including:
o Job submission and scheduling with SLURM.
o Interactive HPC (Rstudio, Jupyter notebooks).
o Running/troubleshooting key MSI bioinformatics pipelines (CHURP, ClusterProfilerWrapper, etc.).

● Direct experience with bioinformatics analysis and handling of tumor/cancer datasets from more than one species.
● Proficiency with Linux and the ability to work from a command line.
● Outstanding laboratory etiquette and evidence of experience working with teams.
● Evidence of collegiality, specifically addressed in letters of reference.
 

Desirable qualifications and skills: In addition to the requirements described above, applicants possessing the skills listed below will be given preference.
ï‚· An advanced degree such as a Masterâ™s of Research or equivalent in bioinformatics/computational biology with a capstone project from an accredited university program. Note that degrees awarded from online-only programs are insufficient to fulfill this qualification.
● Experience performing analyses from open source and custom generated experimental datasets or knowledge of one or more of the following (but not limited to these methods):
o whole exome/whole genome sequencing (WES/WGS).
o assay for transposable accessible chromatin using sequencing (ATAC-seq).
o proteomics (mass spectroscopy data).
o other â˜omics platforms (lipidomics, phosphoproteomics, metabolomics, etc.).
● A strong grasp of MySQL and an ability to operate/handle biological databases.
● Familiarity tracking code/document changes with version control (Git).

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