Study tracks

Medicine is undergoing a transformation due to vast amounts of data generated from patients by new technologies such as the next generation sequencing and high-throughput phenotyping. This so called Big Data is creating great opportunities across fields ranging from personalised cancer therapies to detailed understanding of disease evolution. However, data alone are not sufficient to make scientific or clinical progress. Powerful algorithms as well as computational methods are essential to translate these data into scientific discoveries and clinical useful information.  Bioinformatics and Systems Medicine track prepares students to the new era of biomedicine, where innovative computational approaches are required to interpret  molecular and clinical data from patients.  This specialisation area educates you to be an expert who can turn biomedical questions into appropriate challenges for computational data analysis. For example, we offer biology tailored algorithms and machine learning approaches for analysing molecular data, and computational approaches that allow interpretation of high-throughput biomedical data obtained from patients. The curriculum also includes general algorithm and machine learning studies offered by the Master's Programmes in Computer Science, Data Science, and Genetics and Molecular Biosciences.

Biomathematics with the Biomathematics research group, focusing  on mathematical modelling and analysis of biological phenomena and processes. The research covers a wide spectrum of topics ranging from problems at the molecular level to the structure of populations. To tackle these problems, the research group uses a variety of modelling approaches, most importantly ordinary and partial differential equations, integral equations and stochastic processes. A successful analysis of the models requires the study of pure research in, for instance, the theory of infinite dimensional dynamical systems; such research is also carried out by the group.

Biostatistics study track covers statistical methods for life sciences, with focus on how to make principled statistical inference in real life situations. The methods include, for example, variable selection, statistical clustering, large-scale inference and dimension reduction. Both Bayesian and other approaches are studied. The research applications cover collaborative topics in various biomedical disciplines such as epidemiology of complex diseases, genetic association studies, statistics in medicine and population genetics. Biostatistics track has a close collaboration with Institute for Molecular Medicine Finland (FIMM)

Eco-evolutionary Informatics is a study track where you specialize to mathematical and statistical methods in ecology and evolutionary biology. Ecology studies the distribution and abundance of species, and their interactions with other species and the environment. Evolutionary biology studies processes supporting biodiversity on different levels from genes to populations and ecosystems. These sciences have a key role in responding to global environmental, biodiversity and sustainability challenges. Mathematical and statistical modelling, computer science and bioinformatics have an important role in ecology and evolutionary biology research and their applications. Our researchers and teachers have background in mathematics, statistics and computer science and long experience in applying these methods to ecology and evolutionary biology.