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Paid Internship
Work Mode
Time Spent
Required Degree
Duration

6Open Positions

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Internship in Computational Biology: CVRM Genetics

Roche
Found 3 days ago
Location
Basel, Switzerland
Time
Full-time
Work Mode
On-site
Salary
Not disclosed
Visa Help
Not disclosed
Last Verified
3 days ago

Education

  • Master

Skills & Qualifications

Technical Skills

  • R
  • git/version control
  • Python
  • Plink
  • BCFtools
  • REGENIE
  • GWAS
  • Polygenic Risk Scores (PGS)
  • quantitative statistics
  • data modeling
  • regression
  • linear models
  • hypothesis testing
  • UK Biobank
  • proteomics data
  • HPC clusters
  • Docker
  • Snakemake
  • AI tools
  • Gemini
  • ChatGPT

Soft Skills

  • creative problem-solver
  • quick learner
  • productive when dealing with ambiguity
  • experimenting with new approaches
  • strong collaborator
  • organizational skills
  • initiative
  • communicate effectively and clearly
  • scientific rigor
  • open feedback
  • scientific growth

Job Description

This internship offers the most talented students the opportunity to gain valuable work experience with us. You will work with large-scale genomic data and produce Polygenic Risk Scores (PGS) to investigate the complex genetic architecture of chronic kidney disease (CKD) and metabolic dysfunction (i.e type 2 diabetes, obesity endotypes). As a member of a highly collaborative, multidisciplinary team, you will use your skills to generate significant contributions with practical impact for patients, and you will gain valuable expertise to advance your scientific career. You will be responsible for: building automated PGS workflows to map the complex genetic architecture of Cardio-Renal-Metabolic diseases. Defining mechanistic endotypes through patient clustering and identifying "resilient" outliers—individuals who remain healthy despite high genetic risk—to uncover protective modifiers. The role involves: Mining large-scale genomic, proteomics and electronic health record datasets to drive target discovery. Translating complex genetic architecture into actionable biological insights for drug development. You will gain experience in: high-throughput genomic analysis and advanced patient stratification at a population scale. Precision medicine methodologies and Pharmaceutical R&D workflows, learning how data science directly influences the selection of new therapeutic targets. You will develop skills in collaboration and strategy while working in a team culture that values a collaborative workplace, scientific rigor, open feedback, and scientific growth.

Requirements

  • Master’s degree in Systems Biology, Life Sciences, Biotechnology, Bioengineering or related discipline
  • strong coding experience in R
  • proficiency in git/version control
  • Python or experience developing R packages
  • hands-on experience with genetic data analysis tools such as Plink, BCFtools, or REGENIE
  • foundational understanding of GWAS or Polygenic Risk Scores (PGS)
  • expertise in quantitative statistics and data modeling, including regression, linear models, and hypothesis testing
  • familiar with (or eager to work with) large biobanks (UK Biobank), proteomics data, and computationally intensive environments like HPC clusters, Docker, or Snakemake
  • strong interest in human genetics and metabolic diseases, including the curiosity to apply AI tools (e.g., Gemini, ChatGPT) to novel biological methodologies
  • creative problem-solver and quick learner who remains productive when dealing with ambiguity and experimenting with new approaches
  • strong collaborator with the organizational skills and initiative needed to drive projects toward team goals
  • communicate effectively and clearly in English, both in writing and in spoken discussion
  • CV
  • motivation letter
  • latest certificate of enrolment
  • preferred starting date and duration of the internship on your motivation letter
  • confirmation from the university to the application documents, stating that a mandatory internship is part of the education

Related Field

  • Biology and Biotech

Related Subfield

  • Computational Biology

Languages

  • English

Nice to Haves

  • experience developing R packages
  • familiar with large biobanks (UK Biobank), proteomics data, and computationally intensive environments like HPC clusters, Docker, or Snakemake
  • curiosity to apply AI tools (e.g., Gemini, ChatGPT) to novel biological methodologies
▶Apply Now

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