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Internship in Computational Biology: CVRM Genetics(m/w/x)
Analyzing large-scale genomic data for chronic kidney disease and metabolic dysfunction. Proficiency in R, Python, and genetic analysis tools required. University confirmation for mandatory internship.
Requirements
- Master’s degree in Systems Biology, Life Sciences, Biotechnology, Bioengineering or related discipline
- Strong coding experience in R and proficiency in git/version control
- Proficiency in Python or experience developing R packages
- Hands-on experience with genetic data analysis tools (Plink, BCFtools, REGENIE)
- Foundational understanding of GWAS or Polygenic Risk Scores (PGS)
- Expertise in quantitative statistics and data modeling
- Expertise in regression, linear models, and hypothesis testing
- Familiarity with large biobanks (UK Biobank)
- Familiarity with proteomics data
- Familiarity with computationally intensive environments (HPC clusters, Docker, Snakemake)
- Strong interest in human genetics and metabolic diseases
- Curiosity to apply AI tools (Gemini, ChatGPT) to novel biological methodologies
- Creative problem-solver and quick learner
- Productivity when dealing with ambiguity and experimenting with new approaches
- Strong collaboration skills
- Organizational skills and initiative to drive projects
- Effective and clear communication in English (written and spoken)
Tasks
- Work with large-scale genomic data
- Produce Polygenic Risk Scores (PGS)
- Investigate complex genetic architecture of chronic kidney disease (CKD)
- Explore metabolic dysfunction (type 2 diabetes, obesity endotypes)
- Build automated PGS workflows
- Map complex genetic architecture of Cardio-Renal-Metabolic diseases
- Define mechanistic endotypes through patient clustering
- Identify resilient outliers in genetic risk
- Uncover protective modifiers
- Mine large-scale genomic, proteomics, and electronic health record datasets
- Drive target discovery
- Translate complex genetic architecture into actionable biological insights
- Contribute to drug development
- Gain experience in high-throughput genomic analysis
- Perform advanced patient stratification at population scale
- Learn precision medicine methodologies
- Understand Pharmaceutical R&D workflows
- Influence selection of new therapeutic targets
- Develop collaboration and strategy skills
- Work in a team culture valuing scientific rigor and growth
Education
- Currently in higher education
Languages
- English – Business Fluent
Tools & Technologies
- R
- git
- Python
- Plink
- BCFtools
- REGENIE
- UK Biobank
- HPC clusters
- Docker
- Snakemake
- Gemini
- ChatGPT
Benefits
Other Benefits
- University confirmation for mandatory internship (for non-EU/EFTA citizens)
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Internship in Computational Biology: CVRM Genetics(m/w/x)
Analyzing large-scale genomic data for chronic kidney disease and metabolic dysfunction. Proficiency in R, Python, and genetic analysis tools required. University confirmation for mandatory internship.
Requirements
- Master’s degree in Systems Biology, Life Sciences, Biotechnology, Bioengineering or related discipline
- Strong coding experience in R and proficiency in git/version control
- Proficiency in Python or experience developing R packages
- Hands-on experience with genetic data analysis tools (Plink, BCFtools, REGENIE)
- Foundational understanding of GWAS or Polygenic Risk Scores (PGS)
- Expertise in quantitative statistics and data modeling
- Expertise in regression, linear models, and hypothesis testing
- Familiarity with large biobanks (UK Biobank)
- Familiarity with proteomics data
- Familiarity with computationally intensive environments (HPC clusters, Docker, Snakemake)
- Strong interest in human genetics and metabolic diseases
- Curiosity to apply AI tools (Gemini, ChatGPT) to novel biological methodologies
- Creative problem-solver and quick learner
- Productivity when dealing with ambiguity and experimenting with new approaches
- Strong collaboration skills
- Organizational skills and initiative to drive projects
- Effective and clear communication in English (written and spoken)
Tasks
- Work with large-scale genomic data
- Produce Polygenic Risk Scores (PGS)
- Investigate complex genetic architecture of chronic kidney disease (CKD)
- Explore metabolic dysfunction (type 2 diabetes, obesity endotypes)
- Build automated PGS workflows
- Map complex genetic architecture of Cardio-Renal-Metabolic diseases
- Define mechanistic endotypes through patient clustering
- Identify resilient outliers in genetic risk
- Uncover protective modifiers
- Mine large-scale genomic, proteomics, and electronic health record datasets
- Drive target discovery
- Translate complex genetic architecture into actionable biological insights
- Contribute to drug development
- Gain experience in high-throughput genomic analysis
- Perform advanced patient stratification at population scale
- Learn precision medicine methodologies
- Understand Pharmaceutical R&D workflows
- Influence selection of new therapeutic targets
- Develop collaboration and strategy skills
- Work in a team culture valuing scientific rigor and growth
Education
- Currently in higher education
Languages
- English – Business Fluent
Tools & Technologies
- R
- git
- Python
- Plink
- BCFtools
- REGENIE
- UK Biobank
- HPC clusters
- Docker
- Snakemake
- Gemini
- ChatGPT
Benefits
Other Benefits
- University confirmation for mandatory internship (for non-EU/EFTA citizens)
Like this job?
BetaYour Career Agent finds similar jobs for you every day.
About the Company
Roche
Industry
Pharmaceuticals
Description
The company is dedicated to advancing science and ensuring access to healthcare for everyone.
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