We are looking for the 15 excellent candidates to become the innovation leaders in the vision of a “Personalised In-Silico Cardiology”. We will provide them with a unique opportunity for an exciting 3 year PhD programme coordinated among 10 academic, industrial and clinical institutions, and 9 more associated partners.
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This project has received funding from the European Union’s Horizon 2020 Marie Skłodowska-Curie ITN Project under grant agreement No 764738.
Cardiovascular diseases (CVD) have a huge impact on society in terms of mortality, morbidity and healthcare costs, being responsible for 1.9 million deaths in the EU annually (42% of all deaths) with a total cost of €169 billion. Improving healthcare systems in Europe in a period of ageing population and tightening financial constraints mandates a shift towards personalised and preventive management of disease. We need tailored and earlier treatments to increase the efficacy and efficiency of the healthcare system, as well as the quality of life of patients.
Healthcare provision can conceptually be simplified into three main processes: acquisition of clinical data, diagnosis & therapy planning, and delivery of treatment & intervention. Current technology allows a rich data acquisition, the use of sophisticated devices to monitor patients and deliver care. However clinical practice is guided by the use of averaged (population-based) metrics to define therapy strategies, missing many of the opportunities for disease prevention and tailoring of care for the individual patient.
In this context, recent scientific progress has created an exceptional capacity to simulate in-silico (i.e. on a computer) the heart and its interaction with the circulatory system. Patient-specific in-silico models provide a structured, reproducible and predictive framework for interpreting and integrating clinical data. This provides the pathway for developing personalised and preventive management strategies for cardiovascular diseases. In addition, recent advances in data science (i.e. machine learning, data mining) enable the extraction of novel insights and knowledge from the large repositories of clinical data of our health information systems.
PIC is the European ITN that will train a cohort of 15 future innovation leaders able to articulate and materialise the vision of Personalised In-silico Cardiology (see Fig. 1) where healthcare is guided by in-silico models. These models become virtual reconstructions of an individual, or avatars, to evaluate current health status and therapy options. PIC fellows will build both mechanistic and statistical models from clinical data (WP1), enabling the extraction of biomarkers for better diagnosis and prognosis of the individual patient. PIC fellows will apply models to maximise the value of clinical data (WP2) to inform diagnosis, and to optimise clinical devices & drug choices (WP3) to deliver a personalised therapy.
Fig.1: Illustration of the vision of a personalised in-silico cardiology, where computational models are used to improve the three main processes of healthcare delivery.
Our 15 positions will be opened early September, and the start time must be between September’17 and January’18. We are looking for highly-motivated, inquisitive, enthusiastic, and result-driven candidates, with an excellent academic record and ideally prior research experience. Background either in engineering or biomedical disciplines (check each individual project).
The titles and host institutions of the 15 projects of the PIC fellows are (check eligibility criteria):
These positions have a very attractive financial conditions:
Before considering your application check these two requirements:
Each beneficiary is responsible for the recruitment of the fellows, and each position has also slightly different institutional conditions (i.e. application deadline). Please read the additional details provided in the section below.
Applicants can either submit individual applications to each offer, or fill and submit this joint PIC_Preapplication_form to Elizabeth Baggs firstname.lastname@example.org, including the CV (maximum 2 pages). Note that each position has a different deadline, the most restrictive one is 1st October 2017. Please also note that some of the individual positions will require an additional institutional application.Lee mas