Why study the Master's Degree in Computational Methods in Science?
What will you learn?
- You will be introduced to the science of experimental data processing and analysis.
- As graduate in STEM (Science, technology, engineering and mathematics) you will appreciate the power of quantitative methods and know how to use them at discipline. Programming languages facilitate the task of acquiring, organising, sorting, filtering, processing, refining, representing, and finally explaining scientific data obtained in experimental disciplines.
- Special attention will be paid to computer data processing and analysis, and to the handling of the most commonly used software packages.
- The Master will be organised around the experimental Final Master's Th esis (TFM) on a specific research project, proposed by a topic department . The courses taught will be eminently methodological in nature, on procedures, algorithms and analysis, of a formal nature, sufficiently general for you to apply them under the guidance of tutor in your chosen work.
- There is an optional module containing courses on more specific techniques or procedures in disciplines such as Bioinformatics, Physics and Applied Mathematics, Biology and the Environment or Chemistry.
You will be able to work in...
The Master's degree will provide you with training that will enable you, as an experimental scientist:
- Carry out basic data processing autonomously.
- Interact with specialists (engineers, programmers) and speak their language.
- Start a career in fields such as bioinformatics, machine learning or big data.
Professional projection
Access to doctoral programmes in research lines such as:
- Bioinformatics and massive analysis of biodiversity data.
- Species distribution models and calculation of potential niche.
- Population genetics, metapopulations and microevolution.
- Molecular and morphological taxonomy, phylogeny.
- Ecosystem diversity and environmental impact.
- Air pollution, health and biodiversity loss.
- Natural phenotypic variability and adaptation.
- Territorial connectivity and fragmentation.
- Climate change and biological production.
- Genomics and plant production.
- researcher in the field of biomedicine (research centres, universities, hospitals).
- Bio-informatician in Research Support Platform.
- Developer of new bioinformatics tools (software) .
- for data analysis and interpretation.
- Research and development of new Materials and industrial products.
- Data QualityAssessment.
- development of systems and analysis in proteomics and population genetics.
- DNA sequence management and analysis systems, SNPs and polymorphism studies.
- Analysis and processing of diagnostic imaging technologies.
- Expert in biomedical data analysis at business biotech or pharma.
- consultant of industrial processes.
- Industrial process designer.
- Genomics methods aimed at exploiting natural variability.
- Genetic improvement of production and development of new varieties.
- Agri-food safety through early disease diagnosis methods.
- Management of EST libraries and collections.
- Data processing for cartographic analysis using spectral imaging.
- development of processes for the analysis of species distribution.
- Spatial analysis of changes in land use.
- Management of natural areas and sites of conservation interest.
- Modelling of ecological networks for the control of invasive species.
- Exploitation of large masses of biodiversity data.
- Genomic methods oriented towards the conservation of species in captivity.
- Management and conservation of germplasm banks.