About the Project
About 40% of lower extremity vein grafts occlude or develop significant stenosis already within the first year after implantation. Results for more complex procedures to the calf vessels have usually slightly worse prognosis, with resultant serious morbidity and mortality. In a clinical landscape with ever-increasing and more aggressive bypass procedures, the use of novel engineering simulation tools to understand venous adaptation to the arterial environment and the development of classification tools to understand patient-specific variability would help preventing the significant numbers of excess complications, mortality and cost of re-interventions. This project will create a flexible multi-scale modelling framework to engineer better outcomes for vascular patients undergoing bypass procedures (vein-grafts) and will harness the power of machine learning tools to understand individual variability, classify patients’ risk and predict individual patients’ outcomes.
Person Specification
Knowledge, Education, Qualifications and Training
Essential: MSc or equivalent in Biomedical Engineering, Mechanical Engineering, Aeronautical Engineering, Physics, Applied Mathematics or a related subject.
Experience
• 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: 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 in CFD.
• 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 October/November 2018.
Application Procedure
Eligible applicants should first contact Dr Vanessa Diaz, ([Email Address Removed]) quoting the Job reference. Please enclose a cover letter (including the names and contact details of two referees), one-page research statement and two pages CV. 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.
Funding Notes
Full tuition fees and tax free stipend of £16,777 per annum (for 3 years).
Eligibility: This funding is available for UK and EU passport holders. International students may apply, however, fees will be capped at UK/EU level (students will be required to pay the difference in fees). There is no minimum qualifying residence requirement for applicants from the EU. We actively encourage the application of female applicants for this position. Please DO NOT enquire about this studentship if you are ineligible.
Please refer to the following website for eligibility criteria: https://www.ucl.ac.uk/prospective-students/graduate/research-degrees/mechanical-engineering-mphil-phd
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