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PhD Studentship: Modelling Biological Networks using Omics Data


School of Engineering, University of Warwick

Supervisor: Dr Vishwesh Kulkarni

Project Overview: TIn biological network modelling using metabolomics datasets, the flux balance analysis (FBA) constitutes a key step. These days, increasing amount of additional omics data (such as transcriptomic data, proteomic data, genomic data) is being available and the cost of collecting these datasets is reducing at Moore’s law. How should we use these datasets and the medical imaging datasets so that a more comprehensive model of the underlying biological network is obtained for improved diagnostics, drug treatments, and drug deliveries? In this project, we will develop the theory, algorithms, and case studies to help resolve this question.

Eligibility: UK or EU candidates with a 1st or 2.1 UK Honours degree in subjects such as; Electrical Engineering, Computer Science, biological or biomedical engineering will find this project especially relevant. Experience of mathematical programming in C++ and MATLAB is required. Alternatively, the candidate should be proficient at implementing programmable circuits in wet-lab, either in vivo or in vitro. This project will require a hands-on approach and the expectation is that self-motivated candidates with excellent interpersonal skills and abilities will perform excellent research leading to high quality outcomes and publications.

Funding: The studentship covers 100% tuition fees at the UK/EU rate and standard stipend circa £14,254.

How to apply: To apply for this post you must complete the online application form and quote scholarship reference VK-MS.

As soon as you have a University ID number you will be invited to upload your degree certificate, transcripts, CV and a personal statement that explains your specific research interests and why you should be considered for this award. Application form: www.warwick.ac.uk/pgapply.

Application Form Course Details:
Department: School of Engineering
Course Type: Research
Course: Engineering (PhD)