Research Faculty: Bioinformatics & Proteomics Software Dev

Florida State University; Tallahassee (FL); On-site; USD 85,000 - 125,000; Full time

Job summary

Florida State University invites applications for a Research Faculty position in the Bioinformatics and Proteomics Software Development area within NHMFL. The role focuses on developing algorithms and software for top-down proteomics and metabolomics analyses using FT-ICR and Orbitrap mass spectrometry data.

The successful candidate will collaborate with NHMFL scientists, publish scholarly works, and help secure external funding while maintaining robust, reproducible data analysis pipelines.

Qualifications

• Ph.D. in Bioinformatics, Computer Science, Computational Biology, Analytical Chemistry, Data Science, or a closely related STEM field.
• 2–5 years of additional experience for Research Faculty I; 6–9 years for II; 10+ years for III.
• Proven ability to design, implement, test, and maintain research software and computational workflows in Python.

Responsibilities

• Develop and validate novel algorithms for high-resolution mass spectrometry data analysis.
• Collaborate with NHMFL staff and external users to create robust software tools and workflows.
• Publish manuscripts, present at conferences, and contribute to external research funding proposals.
• Maintain software resources and support users with computational aspects of projects.

Skills

Python

Software development

Scientific programming

Data analysis

GitHub

Education

Ph.D. in Bioinformatics or related STEM field

Tools

GitHub

Open-source software

Job description

Florida State University invites applications for a Research Faculty position in the Bioinformatics and Proteomics Software Development area within NHMFL. The role focuses on developing algorithms and software for top-down proteomics and metabolomics analyses using FT-ICR and Orbitrap mass spectrometry data.

The successful candidate will collaborate with NHMFL scientists, publish scholarly works, and help secure external funding while maintaining robust, reproducible data analysis pipelines.