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The Health and Medical Sciences Programme at Turing delivers research into the theory and methods of AI, statistics, and data analytics underpinning medical and health applications that will enable scientists to do better science, without compromising respect for privacy and patient trust.

Hoffman La-Roche (Roche) has been committed to improving lives since it was founded in 1896 in Basel, Switzerland. Today, Roche creates innovative medicines and diagnostic tests that help millions of patients globally and was one of the first companies to bring targeted treatments to patients.

In 2021 the Alan Turing Institute and Roche initiated a world-leading industry and academic partnership in advanced analytics strategically focused on enabling the transformative benefits of personalized healthcare to become a reality for patients around the world.

The Turing-Roche Strategic Partnership covers multiple activities, with the “North Star” of developing new data science methods to investigate large, complex, clinical and healthcare datasets to better understand how and why patients respond differently to treatment, and how treatment can be improved. Understanding such “treatment heterogeneity” is a problem at the forefront of modern medicine and is an essential first step toward the ambitious goal of developing a personalized healthcare.

ROLES PURPOSE

The Turing-Roche partnership is now recruiting two Senior Research Associates to work on one of these projects:

Conformal Prediction

You will investigate how Conformal Prediction methods can be used to interpret complex healthcare datasets, make more reliable personalized clinical predictions, and improve patient care. Conformal prediction (CP) refers to a very general set of statistical approaches to understand the uncertainties associated with making personalized predictions using a machine learning (or other) model. In a healthcare context, CP tools can enable a more principled understanding of uncertainties made by diagnostic or prognostic models, and so may be vital to translating machine learning methods to the clinic and generating trust in AI. The candidate will have freedom to develop their own programme of work in this area, under supervision, but it is expected to include topics such as: identifying current best practices for incorporating conformal prediction methodologies into clinical AI modelling and testing CP tools on a range of existing models; developing new CP theory for situations in which exchangeability cannot be assumed, for example when there is a dataset drift or a model is being applied in settings beyond which it was developed; extending CP to different loss functions which may be more relevant in a biomedical setting, for example a false negative rate, which may be more directly useful to decision making. These theoretical discoveries are expected to be explored in tandem with their application in healthcare and contribute towards the progression of the clinical AI domain.

Geometry of Deep Learning

You will investigate how tools from geometry and network science can be used to better understand the uncertainty associated with predictions from deep learning models and make recommendations on how this can be minimized in future models. Deep learning represents a very powerful method for learning complex, multiscale patterns in data and is used often in healthcare. However, the fundamental mechanisms by which this learning is achieved are not well understood. This project will use tools from geometry, topology, and network science to derive a more detailed understanding of how deep learning works and how uncertainty propagates in deep neural networks, in order design more robust learning methods that require less tailoring for specific datasets and algorithms. The ultimate aim of this work is to develop theoretical understandings that can then be applied in order to advance deep learning in personalized healthcare applications. The candidate will have freedom to develop their own programme of work in this area, under supervision, but it is expected to include topics such as explore how network tools such as discrete curvature can be applied to interpret deep neural network architectures; exploring geometric approaches to deep learning, using tools from differential geometry; in particular, correspondences between deep learning and Ricci flow; exploring intersections between statistical mechanics and geometric approaches to deep learning, in order to leverage synergies, all developed with a view on applying these learnings to models in healthcare to improve patient outcomes. These theoretical discoveries are expected to be explored in tandem with their application in healthcare and contribute towards the progression of the clinical AI domain.

The successful candidates will also contribute to developing and shaping the direction of research in the Turing-Roche partnership, including working with Turing and Roche scientists on other established research projects. The successful candidate will form part of the Turing’s Health and Medical Sciences programme team, led by Director Professor Chris Holmes and Deputy Director Professor Ben MacArthur and supported by the Business Team for the programme. They will work closely with colleagues at the Turing and advanced analytics leaders and scientists at Roche and will have the opportunity for regular interactions with the partnerships’ advisory group of senior academic experts.

As well as working closely with colleagues at the Roche site at Welwyn within the UK, they will be supported to travel internationally to collaborate with Roche colleagues at other sites (particularly, San Francisco and Basel). The successful candidate will be expected to spend some time working at one or more of the Roche sites to develop relationships and an increased understanding of the pharmaceutical industry.

This is a stand-out opportunity to join a prestigious, national research institute and shape its agenda at an important and exciting time in its development. Working within the partnership will give the successful candidate visibility of many facets of the pharmaceutical industry and support to develop a network of contacts in both academia and industry. As well as being exposed to a variety of analytical methodologies and applications within the partnership they will also have the freedom to develop their own area of aligned research under the mentorship from senior leaders in both academia and industry.

DUTIES AND AREAS OF RESPONSIBILITY

• To assist with the development and delivery of an ambitious programme of research in the chosen area, including generating high quality research outputs, aligned with the aims and objectives of the partnership.
• To undertake high-quality independent research, which will support the partnership in its goal of understanding patient heterogeneity.
• Write or contribute to publications or disseminate research findings using other appropriate media.
• To communicate research outputs to diverse stakeholders, through conferences, events, meetings, and press opportunities as appropriate.
• To work closely with the Turing and Roche communications teams to disseminate and publicise key findings, communicating complex ideas through a variety of mediums.
• Present research updates at meetings both within the Turing and Roche and contribute to both the internal and external visibility of the partnership and the Institute.
• To hold regular meetings with partnership members, and travel as necessary to present work and meet with external collaborators.
• Drive collaboration with academic experts and broader research partners from across the Turing, Roche and the wider Turing / project community.
• Adhere to and promote principles of reproducible and ethical data science
• Ensure compliance with secure handling of data and health and safety in all aspects of work.

• PhD (or equivalent experience and/or qualifications) in a relevant area which will include Statistics, Mathematics, Computer Science, or related discipline
• Substantial experience in statistical modelling and/or data analytics on significant real-world problems, including either knowledge of conformal prediction tools or knowledge of geometry/topology, network science and/or deep learning
• Substantial experience of using modern statistical programming languages (such as R and Python)
• Ability to understand and apply the principles of reproducible data science
• Experience in working with modern artificial intelligence technologies
• Experience of developing and documenting analysis workflows for scientific research projects
• Excellent written and verbal communication skills including the ability to present complex or technical information, and to communicate effectively with analysts and other stakeholders outside the research community
• Ability to collaborate successfully with colleagues in a multidisciplinary environment within the organisation or externally to share knowledge and information in order develop practice or help others learn.

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 and covering letter. If you have questions about the role or would like to apply using a different format, please contact us on 020 3862 3533 or 0203 862 3516, or email [email protected].

If you are applying for more than one role at the Turing, please note that only one Cover Letter can be visible on your profile at one time. If you wish to apply for multiple roles and do not want to overwrite your existing Cover Letter, please apply for the role using the button below and forward your additional cover letter directly to [email protected] quoting the job title.

If you are an internal applicant and wish to apply, please send your CV and Cover Letter directly to [email protected] and your application will be considered.

CLOSING DATE FOR APPLICATIONS: 10 May 2023 at 23:59

TERMS AND CONDITIONS

This full-time post is offered on a fixed term basis for 2 years. The annual salary is £53,576 -£55,125 plus excellent benefits, including flexible working and family friendly policies, https://www.turing.ac.uk/work turing/why-work-turing/employee-benefits

The Alan Turing Institute is based at the British Library, in the heart of London’s Knowledge Quarter. We expect staff to come to our office at least 4 days per month. Some roles may require more days in the office; the hiring manager will be able to confirm this during the interview.

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.

We are committed to building a diverse community and would like our leadership teal to reflect this. We therefore welcome applications from the broadest spectrum of backgrounds.

We are committed to making sure our recruitment process is accessible and inclusive. This includes making reasonable adjustments for candidates who have a disability or long-term condition. Please contact us at [email protected] to find out how we can assist you.

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

Full details on the pre-employment screening process can be requested from [email protected].