PhD Scholarship in Novel Machine Learning Methods for DNA Sequencing

The Department of Computing is a leading department of Computer Science among UK Universities, and has consistently been awarded the highest research rating from the Higher Education Funding Council. In the 2014 REF assessment, the Department was ranked third (1st in the Research Intensity table published by The Times Higher), and was rated as "Excellent" in the previous national assessment of teaching quality.

Imperial’s Department of Computing are seeking a motivated PhD student to work on a project to develop machine learning models and tools to decode signals from DNA sequences faster. The algorithm(s) will be developed and improved before implementation in an open-source and user-friendly software package.

The project is carried out in the context of the EC project OligoArchive (https://oligoarchive.eu) which has the goal of developing a prototype for the storage of information in synthetic DNA. The PhD project will develop the means to decode the synthetic DNA faster through the use of machine learning models and through assumptions about the information stored in the DNA (as opposed to natural DNA where no such assumptions can be made). The post holder will:

• Define benchmark datasets to test the methods and algorithms, Develop the machine learning methods to decode DNA sequences (from DNA storage) faster,
• Integration the code with existing tools and libraries

The post holder will work at Imperial’s SCALE Lab (https://scale.doc.ic.ac.uk). The project allows for some flexibility in the profile of applicants. Candidates with expertise in the following areas can be a good fit:

• Machine learning in its broadest sense,
• Natural language processing,
• DNA, synthetic biology.

All applicants should be able to demonstrate the following:

• A strong computing background with solid programming skills,
• An ability to work with third-party software and to liaise constructively with the developers of such software,
• The ability to work independently and to drive both the research and software development agenda.

The successful applicant will have an MSc (or equivalent) in an area pertinent to the subject area, ideally computer science.

How to apply:
Please forward your CV to Dr. Thomas Heinis: [Email Address Removed]