Within Roche Pharma Research & Early Development (pRED) Pharmaceutical Sciences, the CMI2O (Cardiovascular and Metabolism, Immunology, Infectious Diseases, and Ophthalmology) Systems Biology group is seeking a highly-motivated Master student, dedicated to the development and application of data science tools and systems biology approaches to deepen disease insights and drive target discovery. This internship offers the most talented students the opportunity to gain valuable work experience with us.

You will contribute to our understanding of immune-mediated disorders while being fully immersed in the environment of a global pharmaceutical company. 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.

Degree requirements: 

• You are currently enrolled in a Master’s degree program or a pursued your Master’s degree not longer than 12 months prior to the start datein Systems Biology, Life Sciences, Biotechnology, Bioengineering or related discipline.

Your profile:

• You are a creative problem solver, quick learner and comfortable in experimenting with new approaches; 
• You are a strong collaborator with willingness to initiate projects and contribute to the team goals; 
• You have good organizational skills;
• You demonstrate high productivity and enjoy dealing with ambiguity and applying novel methodologies;
• You have strong communication skills in English (written and spoken).

Required qualifications:

• Strong coding experience: R and Shiny;
• Experience with git and version control;
• Experience with bulk and single-cell RNASeq data analysis and interpretation of results in a disease context;
• Experience with quantitative statistics and data modeling (e.g. hypothesis testing, parametric and non-parametric tests, regression, linear models, etc.).

The following experiences are a plus, but not mandatory:

• Coding experience: Python
• Experience with developing R packages;
• Experience with machine learning algorithms;
• Experience with building and analyzing gene networks; 
• Experience with graph structures, graph search algorithms and graph learning; 
• Experience with technologies required to undertake analyses on large data sources or with computationally intensive steps (parallelization, HPC cluster computing, Docker, LSF);
• Familiarity with human immunology and genetics.