To develop tools for the pre & post-operative in-silico planning and treatment for Aortic Dissections. This project will be an exemplar application that will provide a blueprint and proof of concept for the use of in-silico tools in the personalised management of aortic diseases in general (applicable also to other pathologies). The clinical impact and viability of these technologies will be tested via our partner Hospitals in the UK and Europe. These models will be validated using a sophisticated experimental setup available (including PIV) within the group.
• A patient-specific simulation framework delivering simulation of Aortic Dissections
• Proof-of-concept of the use of these technologies in the clinic
• Proof of feasibility of the use of these models in surgical planning
• Validated set of results using a patient-specific physical platform.
Knowledge, Education, Qualifications and Training
Essential: MSc or equivalent in Biomedical Engineering, Mechanical Engineering, Aeronautical Engineering, Physics, Applied Mathematics or a related subject.
• Essential: Strong background in biomedical engineering, mechanical engineering, aeronautical engineering, physics, applied mathematics; in particular, strong knowledge of how to derive and manipulate differential equations
• Essential: Excellent programming skills in any of the following languages: C, C++, FORTRAN, Matlab, Octave or Python
• Essential: Experience in CFD.
• Essential: Good oral written and presentation skills.
• Essential: Well-organised, attention to detail and ability to meet deadlines.
• Essential: Ability to think logically, create solutions and make informed decisions.
• Desirable: Experience with dynamical systems
• Desirable: Excellent IT skills.
• Desirable: Experience with image processing software, such as MIMICS or ScanIP
Skills and/or Abilities
• Essential: Fluency and clarity in spoken English.
• Essential: Good written English.
• Essential: Independence and ability to work collaboratively as part of a team
Closing Date and Start Date
We will be continuously having informal discussion with interested candidates until this position is filled. The studentship preferred start date is January 2019.
Eligible applicants should contact Dr Vanessa Diaz ([email protected]
) quoting the job reference number. Please forward a CV (including at least two referees), a covering letter and a transcript of results (listing all subjects taken and their corresponding grades/marks) to Dr Diaz ([email protected]
).The supervisory team will arrange interviews for short-listed candidates. After interview, the successful candidate will be required to formally apply online via the UCL website. Regrettably, we are only able to contact candidates who are successful at the shortlisting stage. Thank you for your interest in this position.