Agrobioinformatics

Overview This English-language MSc programme trains graduates who want to apply bioinformatics to agricultural challenges such as food insecurity and climate change. It is aimed at students with a bachelor’s in Agricultural Sciences, Biology, (Bio-)Informatics or related fields and is built to bring together computer science, biotechnology and modern agriculture.

Programme focus You will receive broad computational training to analyse and interpret high-throughput biological datasets — including genomics, proteomics and metabolomics — and learn to build robust analysis pipelines. Course topics range from introductory programming and quantitative genetics to hands-on work with omics data. The curriculum also introduces practical aspects of plant protection, seed production and agrobiotechnology services, covering methods such as genotyping, phenotyping, sequencing and double haploid production that are sought after by upstream production companies.

What you gain Graduates leave with the ability to work with large-scale biological data and to translate bioinformatic methods into agricultural applications. The interdisciplinary training prepares you for roles in biotech service providers, breeding and seed companies, research institutions or for further doctoral study. Being taught in English makes the programme accessible to international students and supports collaboration across the life-sciences and computing disciplines.

Entry requirements (concise)

• Bachelor’s degree in Agricultural Sciences, Biology, (Bio-)Informatics or a closely related discipline
• Strong interest in programming and analysing large biological datasets (Big Data)
• Foundational knowledge in molecular biology, genetics or informatics is advantageous
• Programme language: English — ability to study at master’s level in English is required

Curriculum overview

The programme is built from modular courses; each module carries four semester hours per week and is completed by a final examination. The overall master’s grade is calculated from the individual module grades. Structurally, the degree combines a set of mandatory core modules, a selection of profile (elective) modules, a practical work placement (or additional electives), and a final Master's thesis.

The eight core modules provide a broad, research-oriented foundation in quantitative and experimental agrobioinformatics. Key modules include Applied Statistics; Analysis of DNA and RNA Sequencing Data; Gene Expression Analysis and Systems Biology; Python Programming for Bioinformatics; and Statistical Learning and Neural Networks. Together these cores equip students with computational, statistical and molecular-biology skills needed to analyse high-throughput data and to engage in hypothesis-driven research.

After completing the core curriculum, students personalise their training by selecting profile modules from the faculty catalogue. You can choose six profile modules plus a work placement, or instead take eight profile modules. Example profile areas include Population Genetics; Practical Genome Sequencing and Bioinformatics; Tissue Culturing and Genetic Transformation (lab course); Molecular and Resistance/Quality Plant Breeding; Biodiversity Monitoring with Molecular Tools; and Plant Protection and Bioengineering. A recommended programme schedule is available as a downloadable PDF to help plan coursework, placement and thesis timing.

Requirements (program structure and key modules)

• Each module = 4 semester hours per week and ends with a final exam
• Final programme grade is calculated from achieved module grades
• Programme components: 8 mandatory core modules, profile modules (see options below), a work placement (or two additional profile modules), and the Master’s thesis
• Core modules (mandatory):
  • Applied Statistics
  • Principles of Scientific Practice
  • Analysis of DNA and RNA Sequencing Data
  • Gene Expression Analysis and Systems Biology
  • Python Programming for Bioinformatics
  • Genetics of Crop Diversity
  • Biotechnology and Genomics
  • Statistical Learning and Neural Networks
• Profile module options (examples; choose 6 + placement or 8 total):
  • Population Genetics
  • Practical Genome Sequencing and Bioinformatics
  • Lab Course: Tissue Culturing and Genetic Transformation
  • Molecular Plant Breeding
  • Plant Breeding for Resistance and Quality Breeding
  • Biodiversity Monitoring with Molecular Tools
  • Plant Protection and Bioengineering
• Recommended programme schedule: available as a PDF download

Graduates are prepared for roles that require both computational and agricultural domain expertise. Typical career paths include positions in agri-biotech and seed companies, food industry R&D, chemical and life-science firms, contract sequencing/genotyping service providers, government agencies, and NGOs. The programme also provides a strong foundation for research careers or progression to a PhD in bioinformatics, plant science or related fields.

A voluntary work placement and applied profile modules help students gain hands-on experience and industry contacts, improving employability in upstream production companies and service providers where genomics, phenotyping and data analysis skills are in demand.