The programme is structured to provide both a strong common foundation and personalized specialization paths.

First Year

Depending on their previous preparation, students strengthen either:

  • Core quantitative and computational competencies, or
  • Advanced bioinformatics and AI-driven applications in life sciences.

At the same time, all students consolidate a shared interdisciplinary foundation in:

  • Computational methods and scientific programming
  • Biostatistics and statistical inference
  • Machine learning and artificial intelligence for biological data
  • Genomics, transcriptomics, proteomics and metabolomics
  • Functional and population genetics
  • Systems biology and Structural biology.

Second Year

Students deepen their expertise in:

  • Multi-omics data integration
  • Advanced AI techniques for life sciences
  • Computational structural biology and molecular modelling
  • Modelling and simulation of biological systems
  • Biophysical simulation and molecular dynamics
  • Design and translational development of biotechnological products
  • Innovation management and entrepreneurship in the life sciences sector.

The programme includes:

  • Elective courses to tailor a specific professional profile
  • A compulsory internship
  • A full-semester research thesis based on original work.

The Master’s Degree in Quantitative and Computational Biology equips you with advanced interdisciplinary competencies to understand, analyse and model complex biological systems through quantitative and computational approaches.

You will develop a deep understanding of molecular and cellular biology and learn how to integrate biological knowledge with mathematics, statistics, physics and computer science to address real-world challenges in biotechnology and biomedicine.

Throughout the programme, you will acquire:

  • Advanced scientific programming skills for analysing large-scale biological datasets
  • Statistical inference and multivariate modelling for high-dimensional omics data
  • Machine learning and artificial intelligence methods applied to genomics, transcriptomics and clinical data
  • Multi-omics data integration techniques for extracting meaningful biological insights
  • Mathematical and computational modelling of dynamic biological systems
  • Biological network analysis and systems-level interpretation of complex processes
  • Computational structural biology and molecular modelling for studying biomolecular interactions
  • Simulation methods for investigating biological phenomena across different scales

You will also learn how to:

  • Design reproducible computational workflows and bioinformatics pipelines
  • Critically evaluate data quality, model assumptions and methodological limitations
  • Transform large and heterogeneous datasets into biologically and clinically relevant knowledge
  • Formulate quantitative hypotheses and test them through in silico experimentation
  • Work effectively in interdisciplinary teams at the interface of biology, medicine and data science

Strong emphasis is placed on analytical thinking, problem solving and scientific communication.

By the end of the programme, you will be able to independently design and manage research or innovation projects in data-driven life sciences, operating confidently in both academic and industrial environments.

Teaching methods include:

  • Lectures
  • Computational laboratories
  • Project-based learning
  • Journal clubs and seminars
  • Team-based interdisciplinary work

Strong emphasis is placed on:

  • Hands-on computational training
  • Reproducible research practices
  • Integration between experimental and computational approaches

The second year is largely devoted to internship and thesis research, allowing students to work in cutting-edge laboratories or innovative companies, in Italy or abroad.

Career Opportunities

Graduates are prepared for high-level positions in:

  • Biotech and pharmaceutical industries
  • Computational drug discovery
  • Clinical genomics and precision medicine
  • AI-driven healthcare companies
  • Bioinformatics and omics technology companies
  • Research institutes and innovation centers
  • Data science roles in life sciences

Professional profiles include:

  • Computational Biotechnologist
  • Bioinformatician
  • Computational Biologist
  • Biological Data Scientist
  • Computational Biophysicist

The degree prepares students for professions classified as:

  • Biologists and related professions
  • Biotechnologists
  • Bioinformaticians
  • Data analysts and statisticians
  • Research scientists in life sciences

Advanced studies and research

Graduates can pursue PhD programmes in:

  • Biomolecular Sciences
  • Computational Biology
  • Systems Biology
  • Bioinformatics
  • Biophysics
  • Artificial Intelligence for Health

Graduates are competitive for international doctoral programmes in Europe and beyond.