Deep learning-based autonomous diagnosis of GI (Gastrointestinal) tract cancers
Applications are invited for our EPSRC funded Doctoral Training Partnership (DTP) PhD studentships that will support six (6) research projects starting 1 October 2022. One of these projects is “Deep learning-based autonomous diagnosis of GI (Gastrointestinal) tract cancers” led by Professor Asoke Nandi in the Department of Electronic and Electrical Engineering.
Successful applicants will receive an annual stipend of £18,062 including outer London allowance plus payment of their full-time tuition fees for a period of 42 months (3.5 years).
You should be eligible for home (UK) tuition fees but there is a limited number of studentships (no more than two) available to overseas applicants, including EU nationals, who meet the entry criteria.
Gastrointestinal (GI) tract cancer is the leading cause of all cancer deaths. Deep learning-based diagnosis of GI tract diseases has the potential to revolutionise diagnostic speed, accuracy, and cost. This research will design and develop a deep machine learning-based AI system for polyp’s detection and classification in the GI tract. This project will develop a holistic dataset for GI tract diseases including endoscopy (CE and PE) images in collaboration with a clinical partner.
Please contact Professor Asoke Nandi at email@example.com to find out more.
You will have or will receive an undergraduate degree classified at 1st class or 2:1 (honours) in computer science or an engineering related subject. A postgraduate masters degree may be an advantage. Where appropriate, applicants must have English language proficiency to an overall score of IELTS 6.5 or equivalent.
Skills and Experience
You should demonstrate strong programming skills and experience in machine vision and machine learning algorithms. You should be a highly motivated individual and possess a strong sense of curiosity. The ability to study independently, think critically and collaborate with others is essential.
How to apply
Email the documents below as a single PDF file to firstname.lastname@example.org by 16:00 on Friday 3 June 2022. Please state the name of the project supervisor in your email.
- Your up-to-date CV;
- Your 300-word personal statement setting out why you are suitable for this position;
- Your Undergraduate/Postgraduate Masters degree certificate(s) and transcript(s);
- Your English Language qualification of IELTS 6.5 overall or equivalent, if applicable;
- Two references, one of which can be provided by a member of Brunel University academic staff.
Interviews will take place in June 2022.
- In April 2013, Professor Nandi moved to Brunel University London to become the Head of Electronic and Computer Engineering from the University of Liverpool where he held the David Jardine Chair of Signal Processing in the Department of Electrical Engineering and Electronics. At Liverpool he was the Head of the Signal Processing and Communications Research Group which he established in 1999. Professor Nandi received his PhD from the University of Cambridge. Subsequently, he held positions in Rutherford Appleton Laboratory, CERN, Queen Mary University of London, the University of Oxford, Imperial College London, University of Strathclyde, and the University of Liverpool.
Professor Nandi has published over 250 papers in refereed international journals (total: 600 technical papers) with an h-index of 80 (all citation figures are from Google Scholar) and the ERDOS number of 2. He co-discovered the three particles known as W+, W-, and Z0 - three of the four quanta of the electroweak force. This discovery verified the unification of the electromagnetic force and the nuclear weak force. In its recognition the 1984 Nobel Prize for Physics was awarded to his two leaders for their decisive role in this project. He has made pioneering theoretical and applied contributions to statistical signal processing, wireless communications, machine learning, and biomedical signal processing, image processing, genomic signal processing, brain signal processing, and Big Data.
Professor Nandi is a Fellow of the Royal Academy of Engineering as well as seven other institutions. He was an IEEE Distinguished Lecturer (EMBS, 2018-2019)