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Position: PhD Student - Modelling and Machine Learning to Aid the Design of Cystic Fibrosis Therapy
Institution: University College London
Department: Chemical Engineering / Neuroscience, Physiology and Pharmacology
Location: London, United Kingdom
Duties: Cystic fibrosis (CF) is the most common, lethal genetic disorder amongst Europeans and CF individuals have an average life expectancy of only ~40 yrs. We still do not understand why its absence causes lung disease that is the main cause of morbidity and mortality for CF individuals. To understand the key role played by the CF anion channel is difficult because it is part of a complex system that makes intuitive insight quite limited. This PhD will use the power of computational modelling and machine learning to build a clearer picture of CF airway disease and the specific role of the CF anion channel
Requirements: The applicant should have obtained a First Class Honours degree in engineering or physical sciences. Applicants should have demonstrated numerical and/or computational skills along with a keen interest in biology/health
   
Text: PhD studentship: Modelling and Machine Learning to Aid the Design of Cystic Fibrosis Therapy, - Ref:1808049 Click here to go back to search results UCL Department / Division Department of Chemical Engineering and Department of Neuroscience, Physiology and Pharmacology Location of position London Duration of Studentship 3 years Stipend ?18,000 per annum Vacancy Information This project involves multi-disciplinary team of researchers from Department of Chemical Engineering and Department of Neuroscience, Physiology and Pharmacology. The investigators are members of multi-disciplinary research Centre for Process Systems Engineering (CPSE) and Centre for Computation, Mathematics and Physics in the Life Sciences and Experimental Biology (CoMPLEX). This is a three year project that will use a combination of computational modelling and machine learning to gain a greater understanding of two key regulators of mucociliary clearance: 1) hydration of the airway surface liquid and 2) regulation of viscoelastic properties. The computational modelling component will build on a highly successful model developed for nasal epithelia. This model will be extended to simulate bronchial epithelia by e.g. inclusion of apical potassium channels, and by building in the control of airway surface liquid. The machine learning component of the project will take spinnability measurements of airway surface liquid alongside standard solutions of known elasticity (e.g. collagen) or viscosity (e.g. glycerol), or known mixtures (of glycerol and collagen). Studentship Description Cystic fibrosis (CF) is the most common, lethal genetic disorder amongst Europeans and CF individuals have an average life expectancy of only ~40 yrs. Even though we have known for more than two decades that the defective CF gene encodes an anion channel that is highly expressed in the airways, we still do not understand why its absence causes lung disease that is the main cause of morbidity and mortality for CF individuals. To understand the key role played by the CF anion channel is difficult because it is part of a complex system that makes intuitive insight quite limited. This PhD will use the power of computational modelling and machine learning to build a clearer picture of CF airway disease and the specific role of the CF anion channel. This is an exciting opportunity to use state-of-the-art computational methods to help to solve a major health problem. Apply here: https://www.ucl.ac.uk/adminsys/search/ Person Specification The applicant should have obtained a First Class Honours degree in engineering or physical sciences. Applicants should have demonstrated numerical and/or computational skills along with a keen interest in biology/health. Enquiries should be sent to either Dr. Vivek Dua ( v.dua@ucl.ac.uk ) or Dr. Guy Moss ( g.moss@ucl.ac.uk ). Eligibility The applicant should be a UK/EU citizen. Contact name Dr Vivek Dua Contact details v.dua@ucl.ac.uk UCL Taking Action for Equality Closing Date 14 Jun 2019 Studentship Start Date 1st October 2019 Job description
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