Bioinformatician/Computational Scientist

City of Hope; United States; Sur place; USD 90 000 - 130 000; Plein temps

Résumé du poste

GarMiglio Lab at The Translational Genomics Research Institute (TGen) in Phoenix seeks a computational scientist to develop pipelines and models that integrate spatial single-cell genomics with bulk epigenomics and genomics data. The role emphasizes leadership, data interpretation and preparing manuscripts and grant proposals.

Ideal candidates have 1+ years of research experience, strong Python/R skills, and familiarity with high-performance computing environments such as SLURM.

Qualifications

• Master's or PhD in a related field such as Engineering, Computational Biology, Bioinformatics, CS, Mathematics, or Statistics.
• Deep proficiency or strong interest in molecular cancer biology.
• Knowledge of or experience with basic techniques and methods used in computational genomics.
• Excellent communication and organizational skills.
• Ability to manage time and multitask effectively.

Responsabilités

• Take a leadership role in ongoing projects and develop new research ideas.
• Perform data analysis and interpret results.
• Present findings and prepare peer-reviewed manuscripts and grant proposals.

Connaissances

Python

R

Machine learning

Single-cell genomics

Genomics data integration

Time management

Communication

Formation

Master's or PhD in Engineering/Computational Biology/Bioinformatics/CS/Math/Statistics

Outils

SLURM

Torque/PBS

Bioconductor

Description du poste

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