We are looking for a postdoctoral scholar who can lead a new project which aims to reveal the mechanisms behind the development of asthma in pre-school children. We will develop a mechanistic model (in silico) by integrating clinical data (in vivo) and experimental data (in vitro), to predict which pre-school wheezers will develop asthma and elucidate dynamic patterns underlying the progression of pre-school wheezing. Prior experiences in mechanistic modelling of biological systems and/or in machine learning analysis of clinical data are essential. You will join the team of enthusiastic researchers including computational biologists, mathematicians, experimentalists and clinicians. For further queries, please contact Dr Reiko Tanaka (r.tanaka@imperial.ac.uk).

In a workshop at Cardiff University in Wales, 15 expert speakers showcased some of the ways that machine learning applications and computational methods are driving advances in systems medicine. Machine learning and computational methods in systems medicine are among the keys to advances in healthcare and personalized medicine. These approaches help researchers turn massive amounts of patient data into new treatments and therapies that promote better health and save lives. But what exactly is "systems medicine"? The European Association of Systems Medicine (EASyM), an organization devoted to the topic, offers this definition: "Systems medicine is a novel approach to medicine. It is the first step on the path to personalised medicine. Systems medicine is based on computer models, in which vast amounts of clinical data are used to analyse the health of individual patients."1