The University of Aberdeen is highly regarded for Clinical Pharmacology as the discipline has been taught and delivered for 30 years. It comes from research spanning 50 years. The programme draws on strengths within the university and medical area within disease discovery. Insulin was first developed at University of Aberdeen and the discovery of drug process, treatment and design has been developed and researched ever since. Aberdeen is also known for its research in food and nutrition and other areas. This programme is ideal for newly qualified graduates in medical science disciplines such as biomedical sciences, biochemistry, pharmacology, pharmacy, medicine and similar degrees.
Clinical pharmacology forms a critical part of the drug development process and our graduates are employed in the pharmaceutical and biotechnology industries. These industries are now in rapid growth due to a combination of innovations and strength within customised and other types of medicine and treatment industry areas. Further innovations which link into this industry come from the Internet of Things and more ability to treat and diagnose at source.
There is always a strong need for the discipline to provide a foundation to any new innovations which often come from multidisciplinary teams. Our aim is to train students in the major areas of clinical pharmacology including molecular pharmacology, drug metabolism and toxicology, therapeutics, pharmacokinetics, pharmcovigilance, regulatory affairs and experimental medicine. The programme aims to achieve this by a multi-disciplinary approach.
Drug Metabolism and Toxicology
Basic Skills- Induction
Drug Development to Evidence Based Medicine
Basic Research Methods
Business of Science
Health Informatics (distance learning
Find out more detail by visiting the programme web page
Find out about fees:
*Please be advised that some programmes have different tuition fees from those listed above and that some programmes also have additional costs.
View all funding options on our funding database via the programme page
Find out more about:
Find out more about living in Aberdeen:
Health informatics studies the nature of medical data and the use of information technology to manage health-related information in medical practice, education, and research. With increases in the application and uses of information technology in the medical industry, there is an unprecedented need for professionals who can combine their knowledge of computing and health care to improve the safety and quality of care delivery, as well as to help control costs.
The MS degree in health informatics applies the creative power of information technology to the information and data needs of health care. This includes the acquisition, storage, and retrieval of patient data, as well as access to electronically maintained medical knowledge for use in patient care, research, and education. Professionals in the field require computing expertise; an understanding of formal medical terminology, clinical processes, and guidelines; and an understanding of how information and communication systems can be used to successfully deliver patient information in various health care settings. The program is offered online only.
The program offers two tracks: the clinician track and analyst track.
To be considered for admission into the MS program in health informatics, candidates must fulfill the following requirements:
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.