The Alan Turing Institute is the UK’s national institute for data science and artificial intelligence. The Institute is named in honour of the scientist Alan Turing and its mission is to make great leaps in data science and artificial intelligence research in order to change the world for the better.

Clinical trials are the gold standard for testing treatments (drugs, surgical procedures, or other health interventions) in clinical care. Because we rely on trial results for treatment approvals, the UK government has earmark methodological developments in clinical trials as an area where the UK aspires to be world-leading.

The post-holder will explore existing, and develop novel, machine learning methods for two of the challenges faced by clinical trials. These are:

1. Treatment effect heterogeneity and subgroup discovery: identifying which patients may benefit from treatment. The aim is to use data from large randomised trials to develop novel statistical machine learning methods for the identification of treatment effect heterogeneity in clinical trials, as well as building counterfactual predictive models leading to personalised treatment effects. Retrospective analysis in clinical trials enables the data-driven detection of subgroups of patients who may benefit from a particular course of treatment, thereby providing the basis for future trials.
2. Improving quality assurance processes. Clinical trials typically involve patients recruited and followed up by multiple sites (usually hospitals). Trial monitoring is a necessary task in all clinical trials where the central trial team assesses performance at each site. Poor performance indicators are aggregated in a pre-specified way; if a threshold is exceeded, the trial team then visits the site to ensure that all applicable ethical and regulatory requirements, guaranteeing participants’ safety and data robustness, are being adhered to. However, currently there is doubt that this process adequately targets sites at risk of non-adherence. The aim of this project is to explore AI / machine learning approaches to identify and predict which sites within an ongoing clinical trial are underperforming or at risk of non-adherence, using centrally held patient-reported data, and previous longitudinal site monitoring data, which includes time-series data and free text. These predictions will assist in the prioritisation and planning of monitoring actions (e.g. site visits).

Particular attention will be paid to issues around interpretability, reproducible research, methods validation and the assessment of false discovery rates.

Successful candidates should have a strong quantitative background and preferably a strong machine learning and statistics background and be eager to do research at the interface of AI and clinical trials.

ORGANISATIONAL POSITION

The postholder will be based at the Alan Turing Institute, and work closely with senior collaborators at the Alan Turing Institute and the MRC Clinical Trials Unit (MRC CTU) at UCL.

The successful candidate will be a key member of the project team, playing a central part in shaping the project, assuming responsibility for its successful delivery, conducting research, and taking primary responsibility for the writing of academic manuscripts, both of novel research and tutorial papers giving guidance to the clinical trial community. These will represent the project’s main deliverables. Other important components of the role include engagement in collaborative and knowledge-sharing activities with other clinical trial researchers and CTUs, and organising activities aimed at communicating project findings to academic and stakeholder audiences.

Depending on the background of the successful candidate, there will be opportunities for undertaking appropriate training in Clinical Trial design, conduct and analysis.

The post holder will:

• Explore the potential of new statistical machine learning methods to improve the learning opportunities from clinical trials;
• Undertake analyses using machine learning methods on existing trials data held within the MRC CTU;
• Present research findings through academic papers and presentations at academic and professional conferences, and to contribute to the external visibility of both institutes;
• Participate in the organisation of research workshops and other events.

The post holder will be expected to work collaboratively with the senior investigators from across the Turing and MRC CTU as well as with external partners on the project. At the same time, you will be given the personal freedom to develop and pursue innovative research ideas.

BACKGROUND

The Alan Turing Institute in partnership with the MRC Clinical Trials Unit at UCL is offering an exciting job at the interface of machine learning and biostatistics. This is an opportunity for a data scientist, machine learning researcher or statistician with interests both in the development of machine learning methodology and in medical research to join Professor Chris Holmes and Dr Karla Diaz-Ordaz’s causal machine learning research group.

More information about the project can be found here: https://www.turing.ac.uk/research/research-projects/statistical-machine-learning-randomized-clinical-trials-mrc-ctu

MRC Clinical Trials Unit at UCL (MRC CTU)

The MRC CTU is a centre of excellence for clinical trials, meta-analyses and epidemiological studies. It is committed to strengthening and expanding the evidence base for healthcare nationally and internationally. It also develops methodology to improve the design, conduct and analysis of clinical studies, and hosts one of the MRC’s eight regional Hubs for Trials Methodology Research. The MRC CTU currently employs around 230 staff. Further details can be found at its website: http://www.ctu.mrc.ac.uk/.

This post is supported by Wave 1 of The UKRI Strategic Priorities Fund under the EPSRC Grant EP/T001569/1, particularly the “Health” theme within that grant and The Alan Turing Institute: https://www.turing.ac.uk/research/asg

Duties and Responsibilities

• To take initiatives in the planning and execution of research;
• To conduct data analysis: including preparing the data (cleaning, feature engineering, visualization, etc);
• To assist or lead on the data study groups (Turing Institute datathons) associated with this collaboration ;
• To ensure the validity and reliability of data at all times;
• To maintain accurate and complete records of all findings;
• To undertake any training and or professional development;
• To prepare material for presentation in oral and poster formats;
• To draft publications and prepare them for submission to refereed journals;
• To write and publish articles in peer-reviewed journals/digests that highlight findings from research ensuring consistency with the highest standards of academic publication and showcasing the Institute’s research leadership;
• To contribute to writing bids for research grants;
• To supervise practical work and advise students/other researchers associated with the project on techniques;
• To take responsibility for organising resources and effective decision making in support of research;
• To attend relevant workshops and conferences as necessary;
• To deliver training materials (slides, notes, and presentations) and related activities as required as part of collaboration work.
• To work in close co-operation with the principal investigator and collaborators on the project;
• To provide regular updates on progress to the team;
• To undertake appropriate administration tasks;
• Support the Principal Investigator and research group in the design and development of the research programme.

If appointed at a Senior Research Associate level, the post-holder will have additional responsibilities, such as:

• To oversee the work of other Research Associates who are conducting research in related areas.
• To define the research direction in collaboration with the PIs of the project;
• Extend, transform and apply knowledge acquired from scholarship to research and appropriate external activities;
• Assist and then lead in the preparation of proposals and applications to external bodies, e.g. for funding and contractual purposes;
• Present data and findings to the study team or for other audiences, using appropriate media and platforms;
• Support externally funded research projects, and to identify and work with senior colleagues to develop research ideas and contribute to funding proposals.

Essential

• Research Associate/Senior Research Associate level: Holds a PhD or equivalent level of professional qualification in a field with significant element of computational statistics or statistical machine learning;
• Experience in the development and/or application of statistical machine learning methods;
• Experience in managing, structuring, and analysing research data;
• Fluency in one or more modern statistical programming languages used in research in data science and artificial intelligence (preferable R or Python);
• Interest and/or knowledge of methodological (statistical or AI) advances in medical applications or clinical trials;
• Ability to rapidly assimilate new computational and statistical ideas and techniques on the job and apply them successfully;
• Excellent written and verbal communication skills, including experience in the visual representation of quantitative data, the ability to write for publication, present research proposals and results, and represent the research group at meetings;
• Ability to communicate complex information clearly;
• Ability to work with others, especially postdocs, research assistants and PhD students;
• Ability to direct the work of a small research team and motivate others to produce a high standard of work (for Senior Level);
• Ability to lead one’s own work independently, including planning and execution, and to collaborate productively as part of a team;
• Ability to organise and prioritise own work with minimal supervision;
• Ability to carry out original research and to produce published research papers;
• Previous experience of conducting studies of related literature and research to support the design and implementation of projects;
• Previous experience and ability of developing reports, ensuring conceptual relevance, comprehensiveness, and currency of information;
• Ability to identify, develop and apply new concepts, techniques and methods;
• Commitment to meeting deadlines;
• Flexible attitude towards work;
• Commitment to EDI principles and to the Organisation values;

Desirable

• Computational statistics, particularly Bayesian modelling and Bayesian statistics and graphical methods and data visualisation;
• Experience in producing reliable software and reproducible research (e.g. version control such as Git, literate analysis tools such as Jupyter and Rmarkdown);
• An understanding of clinical trials, particularly the statistical methods used to analyse data from clinical trials;
• Ability to direct the work of a small research team and motivate others to produce a high standard of work;
• Ability to encourage research culture in others;
• Creative approach to problem solving.

Term and Conditions

This full-time post is offered on a fixed-term basis for two years. The annual salary is £35,000 - £41,000 (dependent on skills and experience) plus excellent benefits, including flexible working and family friendly policies, https://www.turing.ac.uk/work-turing/why-work-turing/employee-benefits

For appointments at the senior associate level the salary range is £42,000 - £49,000.

Please note: Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant within the salary range £32,000-£34,000 per annum.

This job description is written at a specific time and is subject to change as the demands of the Institute and the role develop. The role requires flexibility and adaptability, and the post holder needs to be aware that they may be asked to perform tasks and be given responsibilities not detailed in this job description.

Application procedure

If you are interested in this opportunity, please click the apply button below. You will need to register on the applicant portal and complete the application form including your CV, covering letter and contact details for your referees.

Along with a CV and covering letter, please submit a research output to support your application, for us to read before the interview. This might be a link to a selected research or technical paper, a technical blog post or a chapter of a thesis or dissertation, but we particularly encourage applicants to submit a link to a public version control tool such as GitHub containing an example analysis script or research software library you have made a significant contribution to.

If you have questions about the role or would like to apply using a different format, please contact them on 020 3862 3575, or email recruitment@turing.ac.uk.

Secondments from University partners are welcome.

Closing date for applications: 29 November 2020

Equality Diversity and Inclusion

The Alan Turing Institute is committed to creating an environment where diversity is valued and everyone is treated fairly. In accordance with the Equality Act, we welcome applications from anyone who meets the specific criteria of the post regardless of age, disability, ethnicity, gender reassignment, marital or civil partnership status, pregnancy and maternity, religion or belief, sex and sexual orientation.

Reasonable adjustments to the interview process can also be made for any candidates with a disability.

Please note all offers of employment are subject to continuous eligibility to work in the UK and satisfactory pre-employment security screening which includes a DBS Check.