PhD studentship in Machine Learning for Synthetic Biology (EPSRC Portabolomics)

Postgraduate Opportunities

Newcastle University

Reference code: COMP002

Closing date: 13 March 2018

Supervisors: Dr Jaume Bacardit, School of Computing, Newcastle University.

Sponsor: School of Computing, Newcastle University

Duration of the award: 3 years

Person Specification

Applicants should have a first class degree, or a combination of qualifications and/or experience equivalent to that level. Ideally, students should have a BSc or MSc degree in computer science. Applicants should be strong programmers, and experience in machine learning/data mining/big data/information visualisation/biological data will be greatly valued.


This PhD studentship is part of the Portabolomics project. The vision of Portabolomics is to bring forth a breakthrough in Synthetic Biology that will enable the development of portable biocircuits across chassis (i.e. from one bacteria species to another). This vision is akin to the Java virtual machine having enabled the reuse and portability of software across different operating systems and hardware platforms.

In this doctoral project you will focus on the challenge of devising innovative strategies to transform the vast volumes of data generated in the wet lab experiments of Portabolomics into actionable knowledge that can feed into the computational work on network analysis and verification in the project. The data generated by the project is vast and diverse: imaging data, omics data, complex and heterogeneous annotation from public and private sources. Using a combination of biological data integration, state-of-the-art machine learning, knowledge extraction and information visualisation techniques we seek to build methods to identify biomarkers and infer biological networks.

The specific topic of each studentship will be decided based on the skill set of the successful applicants, although we envision that they will require a combination of the following:

- Strong machine learning background and proficiency in the state of the art data science languages (e.g. R, python)
- Deep Learning
- Knowledge discovery
- Biological data integration
- Information visualisation
- High Performance Computing (e.g. classic HPC clusters, GPUs, Intel PHI, Big Data frameworks, Cloud resources).

Study information

Start month:

September 2018

Funding information

Funding applies to:
Open to applicants from a range of countries
Funding notes:

100% of UK/EU tuition fees paid and annual living expenses of £14,553(full award). Successful international candidates will be required to make up the difference between the UK/EU fees and international fees.

Contacts and how to apply

Academic contact:

For informal enquiries, please email

Administrative contact and how to apply:

You must apply through the University's Application Portal. Only mandatory fields need to be completed. You will need to include the following information:

- select 8050F as programme code
- select ‘PhD in Computer Science (FT) - Computer Science’ as the programme of study
- insert the studentship code COMP002 in the studentship/partnership reference field
- attach a covering letter and CV. The covering letter must state the title of the studentship, quote reference code COMP002 and state how your interests and experience relate to the project.
- attach degree transcripts and certificates and, if English is not your first language, a copy of your English language qualifications.

Please also send the covering letter and CV to

Application deadline:

13 March 2018