Education

The Biostatistics Department is actively engaged in the academic and teaching mission of the CING. The Department contributes to graduate education by delivering lectures within core courses, including:

• Molecular Basis of Complex Diseases (MM102)
• Methodologies and Technologies Applied in Medical Genetics (MG103)
• Bioinformatics (BMI101)

In addition, department members provide supervision and mentoring for MSc and PhD students, supporting advanced training in biostatistics, statistical genetics, and genetic epidemiology.

Graduate Training and Research Experience

Students joining the Biostatistics Department gain hands-on experience in the development and application of cutting-edge statistical methods to investigate complex diseases, with a particular focus on breast cancer. Training includes:

• Statistical analysis of large-scale genetic and genomic datasets
• Methods in statistical genetics and genetic epidemiology
• Programming in R for statistical computing and data analysis
• Use of command-line tools and high-performance computing environments

Students are fully integrated into ongoing research projects and are encouraged to disseminate their work through poster and oral presentations at national and international conferences. This engagement provides valuable experience in scientific communication and professional development.

Through this training, students develop strong problem-solving abilities, critical thinking skills, and methodological expertise, preparing them for careers in academia, research institutions, industry, and healthcare.

Internships and Short-Term Training

The Biostatistics Department offers undergraduate and postgraduate internship opportunities upon request, typically during the summer period. Short-term placements (approximately one week) are designed to provide:

• An overview of the Department’s research activities
• An introduction to biostatistics and statistical genetics
• Basic training in the R programming language
• Experience working on a small project in a real research environment

These internships offer an early exposure to quantitative biomedical research and support informed career choices in data-driven life sciences.