Bioinformatician/Computational Scientist

City of Hope; Phoenix (AZ); On-site; USD 90,000 - 120,000; Full time

Job summary

The GarMiglio Lab at The Translational Genomics Research Institute (TGen) in Phoenix seeks a talented Computational Scientist to develop computational pipelines and data analytical models. Focus areas include spatial single-cell genomics and bulk epigenomics with an emphasis on cancer biology.

The candidate will lead data analysis, interpret results, and contribute to peer‑reviewed manuscripts and grant proposals. Strong coding skills in Python/R and HPC experience are expected.

Qualifications

• Master's or PhD (optional) in Engineering, Computational Biology, Bioinformatics, CS, Mathematics or related field.
• Deep proficiency or strong interest in molecular cancer biology.
• Knowledge of basic techniques and methods required by the position.
• Excellent communication and organizational skills.
• Ability to manage time and multitask effectively.

Responsibilities

• Data analysis and interpretation of results.
• Presentation of findings and preparation of peer‑reviewed manuscripts and grant proposals.
• Develop computational pipelines and data analytic models for integrating spatial single‑cell genomics and bulk (epi)genomics.

Skills

Python

R

Machine learning

Genomics data analysis

Bash

HPC

Education

Master's degree in Computational Biology / related field

PhD in related field (optional)

Tools

Bioconductor

SLURM/Torque

Linux

Job description

GarMiglio Lab - Computational Scientist Roles

The Translational Genomics Research Institute (TGen), Phoenix

Bioinnovation and Genome Sciences Division (PI Dr. Luciano Garofano, PhD)

Clinical Genomics and Therapeutics Division (PI Dr. Simona Migliozzi, PhD)

The GarMiglio Lab (https://garmigliolab.com/) led by Dr. Luciano Garofano (Assistant Professor) and Dr. Simona Migliozzi (Assistant Professor) at The Translational Genomics Research Institute (https://www.tgen.org, located in Phoenix) is an interdisciplinary research group aimed at understanding the molecular mechanisms of cancer progression, metastasis, and resistance to therapy in solid tumors with particular focus on adult and pediatric brain tumors, lung, breast, melanoma and brain metastasis. Our groups combine patient biospecimens, computational approaches and experimental models to dissect cancer heterogeneity, identify functional and clinically relevant tumor subtypes in order to extract targetable molecular nodes with the final aim of developing more effective therapies that improve the survival outcome of cancer patients. Ongoing projects include: i) dissecting glioma ecosystem during evolution, ii) investigating the role of neurons-cancer crosstalk in non-CNS tumor progression, iii) reconstructing the spatiotemporal proteomic mosaicism in pediatric brain tumors.

We are looking for talented researchers to develop computational pipelines and implement data analytical models to integrate spatial single-cell genomics and bulk (epi)genomics. Candidates with expertise in machine learning approaches for integrating WES and RNA-seq data, including somatic variant calling from tumor–normal samples and CNV detection for clonal reconstruction, as well as knowledge of or a strong interest in cancer biology, are encouraged to apply.

The successful candidate will take a leadership role in ongoing projects as well as develop new research ideas. Specific duties will include, data analysis, interpretation of results, presentation of findings and preparation of peer reviewed manuscripts and grant proposals.

Required Qualifications:

• ? Master’s orPhD (optional) in Engineering, Computational Biology, Bioinformatics, Computer Science, Mathematics, Statistics, or related field.
• ? Deep proficiency or strong interest in molecular cancer biology.
• ? Knowledge of, or experience with, basic techniques and methods required by the position.
• ? Excellent communication and organizational skills.
• ? Ability to manage time and multitask effectively.

Preferred Qualifications:

• ? At least 1 year of research experience.
• ? Experience coding in bash, Python and/or R environments.
• ? Experience with Bioconductor packages.
• ? Confident operating high-performance cluster systems via schedulers such as SLURM or Torque/PBS