Onsite Environmental Molecular Bioinformatician — 2‑Year Term

Stanford University; Monterey (CA); Sur place; USD 86 000 - 104 000; Plein temps

Résumé du poste

Stanford University’s Woods Institute for the Environment invites applications for an Environmental Molecular Bioinformatician at the Hopkins Marine Station in Pacific Grove, CA. This two-year fixed-term role focuses on setting up molecular lab workflows, metabarcoding, and data pipelines to study regional biodiversity and ocean health.

You will collaborate with partners in California and Palau, develop databases for long-term storage, analyze sequencing data, and build reproducible workflows

Qualifications

• Experience setting up molecular lab workflows and data management.
• Experience with Illumina and Oxford Nanopore sequencing platforms.
• Proficiency in R, Python, SQL and Unix-based environments.
• Ability to develop reproducible data workflows and databases.

Responsabilités

• Seawater eDNA sample processing and sequencing workflow setup.
• Design and optimize sequencing data pipelines and analyses.
• Develop and maintain databases for long-term biodiversity data.
• Create reproducible workflows that meet international data standards.
• Collaborate with partners in California and Palau on projects.
• Analyze sequencing data and interpret ecological patterns.

Connaissances

R programming language

Data management

Bioinformatics pipelines

Database management

Collaborative mindset

Lab workflows

Outils

Illumina sequencing

Oxford Nanopore sequencing

UNIX

Python

SQL

Description du poste

Stanford University’s Woods Institute for the Environment invites applications for an Environmental Molecular Bioinformatician at the Hopkins Marine Station in Pacific Grove, CA. This two-year fixed-term role focuses on setting up molecular lab workflows, metabarcoding, and data pipelines to study regional biodiversity and ocean health.

You will collaborate with partners in California and Palau, develop databases for long-term storage, analyze sequencing data, and build reproducible workflows