Computational Biology Intern — HPC Research

Barcelona Supercomputing Center; Barcelona; On-site; EUR 12,000 - 17,000; Full time

Benefits offered by this job

Full-time internship (35h/week)

Good working environment

State-of-the-art infrastructure

Job summary

The Barcelona Supercomputing Center (BSC-CNS) invites applications for an undergraduate student position in Computational Biology (R0). The role offers a full-time internship (35h/week) in a high-impact HPC environment using MareNostrum 5, with international and multidisciplinary teams.

The candidate will work within the Computational Biology group, contributing to multi-omic data analyses and disease co-occurrence research.

Qualifications

• Basic programming skills in Python and/or R.
• Familiarity with UNIX/Linux environments.
• Understanding of bioinformatics or computational biology workflows.
• Ability to interpret scientific literature.

Responsibilities

• Analyze and preprocess biomedical datasets, including databases, genomic and transcriptomic data.
• Develop, implement, and optimize computational pipelines and tools for disease research.
• Apply computational methods to a specific biomedical case study and generate interpretable results.
• Document and present results in internal meetings and contribute to research outputs.
• Participate in internal group meetings and other scientific discussions.

Skills

Python

R

UNIX/Linux

Bioinformatics workflows

Literature interpretation

Education

Bachelor's degree in Computer Science, Bioinformatics, Computational Biology, Life Sciences

Tools

Computational pipelines

High-performance computing (HPC)

Job description

The Barcelona Supercomputing Center (BSC-CNS) invites applications for an undergraduate student position in Computational Biology (R0). The role offers a full-time internship (35h/week) in a high-impact HPC environment using MareNostrum 5, with international and multidisciplinary teams.

The candidate will work within the Computational Biology group, contributing to multi-omic data analyses and disease co-occurrence research.