This new and innovative course builds upon the integrated nature of the School of Dentistry’s clinical and basic science divisions, and aims to prepare future researchers, from scientific or clinical backgrounds for research careers based in addressing oral health needs. You’ll gain a thorough background in oral sciences, the investigative, cutting edge technologies that enable oral scientific discovery and the necessary training in research governance and rigour. All areas of translational research pathways will be addressed, including aspects of commercialisation which will be taught through the Leeds University Business School (LUBS). Disease focused modules provide opportunities for in-depth exploration with research experts in the fields of Cancer, Musculoskeletal and Oral and systemic disease links.
Our teaching staff includes world leading experts with track records in translating research discoveries into novel healthcare products and practices. Student integration within the wider Dental school will be facilitated by undertaking recently updated modules shared with students from other MSc programmes.
Aimed at dental and biosciences graduates, the course will facilitate a career path focussed on oral research and its translation into positive impacts on health.
The programme will:
Teaching will be split between the Dental school on the main campus and the Wellcome Trust Brenner Building (WTBB) at the St James’s University Hospital. The WTBB is a modern purpose built research facility, housing cutting edge facilities in imaging, tissue and microbiological culture and next generation sequencing technologies. On the main campus students can benefit from all the expertise, facilities (such as the Leeds Dental Translational and Clinical Research Unit) and support provided by the Dental school.
Our course emphasises student directed and multidisciplinary learning. Teaching methods include lectures, seminars and workshops, complemented by e-learning and will be delivered by research active scientists and clinicians with additional input from industrial partners and Leeds University Business School (LUBS) academics.
Summative assessment will provide you with on-going feedback on your depth of subject knowledge and skills. Assessment methods for formative and summative assessment will include oral and poster presentations, unseen examinations and literature reviews. Exercises to identify research questions formulate research plans and prepare mock applications for funding and ethical/ governance approvals will also contribute to assessment.
You will gain insight into all stages of translational research, preparing you for a career working across multi-disciplinary teams within research and innovation management. The course aims to enhance your career prospects of securing PhD studentship positions, whether that be in pre-clinical or clinical research.
The innovation management in practice module enables you to learn about the commercial aspects of translational research. It may be that you want to go into the oral healthcare industry, so knowledge of business skills will be a useful transferable skill.
You may want to go into academic teaching positions within your own country; this MSc will provide the knowledge required to teach oral biology at undergraduate level.
Life Sciences is one of the strategic research fields at the University of Helsinki. The multidisciplinary Master’s Programme in Life Science Informatics (LSI) integrates research excellence and research infrastructures in the Helsinki Institute of Life Sciences (HiLIFE).
The Master's Programme is offered by the Faculty of Science. Teaching is offered in co-operation with the Faculty of Medicine and the Faculty of Biological and Environmental Sciences. As a student, you will gain access to active research communities on three campuses: Kumpula, Viikki, and Meilahti. The unique combination of study opportunities tailored from the offering of the three campuses provides an attractive educational profile. The LSI programme is designed for students with a background in mathematics, computer science and statistics, as well as for students with these disciplines as a minor in their bachelor’s degree, with their major being, for example, ecology, evolutionary biology or genetics. As a graduate of the LSI programme you will:
Further information about the studies on the Master's programme website.
The Life Science Informatics Master’s Programme has six specialisation areas, each anchored in its own research group or groups.
Algorithmic bioinformatics with the Genome-scale algorithmics, Combinatorial Pattern Matching, and Practical Algorithms and Data Structures on Strings research groups. This specialisation area educates you to be an algorithm expert who can turn biological questions into appropriate challenges for computational data analysis. In addition to the tailored algorithm studies for analysing molecular biology measurement data, the curriculum includes general algorithm and machine learning studies offered by the Master's Programmes in Computer Science and Data Science.
Applied bioinformatics, jointly with The Institute of Biotechnology and genetics.Bioinformatics has become an integral part of biological research, where innovative computational approaches are often required to achieve high-impact findings in an increasingly data-dense environment. Studies in applied bioinformatics prepare you for a post as a bioinformatics expert in a genomics research lab, working with processing, analysing and interpreting Next-Generation Sequencing (NGS) data, and working with integrated analysis of genomic and other biological data, and population genetics.
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 and bioinformatics is offered jointly by the statistics curriculum, the Master´s Programme in Mathematics and Statistics and the research groups Statistical and Translational Genetics, Computational Genomics and Computational Systems Medicine in FIMM. Topics and themes include statistical, especially Bayesian methodologies for the life sciences, with research focusing on modelling and analysis of biological phenomena and processes. The research covers a wide spectrum of collaborative topics in various biomedical disciplines. In particular, research and teaching address questions of population genetics, phylogenetic inference, genome-wide association studies and epidemiology of complex diseases.
Eco-evolutionary Informatics with ecology and evolutionary biology, in which several researchers and teachers have a background in mathematics, statistics and computer science. 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 challenges. Mathematical and statistical modelling, computer science and bioinformatics have an important role in research and teaching.
Systems biology and medicine with the Genome-scale Biology Research Program in Biomedicum. The focus is to understand and find effective means to overcome drug resistance in cancers. The approach is to use systems biology, i.e., integration of large and complex molecular and clinical data (big data) from cancer patients with computational methods and wet lab experiments, to identify efficient patient-specific therapeutic targets. Particular interest is focused on developing and applying machine learning based methods that enable integration of various types of molecular data (DNA, RNA, proteomics, etc.) to clinical information.