Research Associate - Real World Learning

Alan Turing Institute London United Kingdom Research Programmes
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Company Description

Named in honour of Alan Turing, the Institute is a place for inspiring, exciting work and we need passionate, sharp, and innovative people who want to use their skills to contribute to our mission to make great leaps in data science and AI research to change the world for the better.

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The Alan Turing Institute has been awarded US Army Research Laboratory (ARL) funding for a project on real world learning through advanced synthetic data approaches.

We are looking to build a team of people who are passionate about science, technology, and innovation, to work with some of the best scientific minds in AI in the defence and security domain. The research associate would work on AI approaches to the fusion and analysis of complex, multi-modal, high dimensional streams of electro-optical and infrared data. Your role will be to work in the recently launched Defence AI centre (ARC-D), both independently and collaboratively with the PIs and other researchers in the group working in domains as diverse as: future sensing, space systems, human-machine teaming, embedded hardware and software, electromagnetic activities, and communications and networks. The ideal candidate is inquisitive, enjoys solving complex, challenging problems, and thinks creatively to find non-obvious solutions.

The defence and security programme at the Alan Turing Institute delivers an ambitious programme of data science and artificial intelligence research that will impact real world scenarios. We provide a rewarding, fast-paced and innovative environment with the opportunity to get close to the application, and work embedded with defence partners with a wide range of expertise.


To advance the state of the art there is a need to develop AI models that generate a richer representation of the world, much more akin to the human ‘embodied’ experience of learning from continuous streams of data. The challenge is that access to limited (or erroneous) data requires the remainder of the ‘picture’ to be constructed, whilst maintaining the integrity of the world that we are trying to represent. The DEVCOM Army Research Laboratory (ARL) identifies this gap and knowledge requirement as “synthesis techniques and data sets that support multimodal interactive learning” (Rao et al., 2021).

You will develop novel methods to realise a multi-perspective data environment that enables the fusion of electro-optical and infrared data at multiple spatial and temporal scales. Your work will address three key challenges: synthetic stream data generation, synthetic generation of plausible future scenarios and synthetic generation of edge cases. The techniques developed will help to close the gap between synthetic data generation and real data collections and enable the transferral of AI techniques between different domains. You will pair existing, maturing, and emerging algorithms with synthetic data environments to rapidly evolve autonomous behaviours to detect anomalies and outliers and to perceive meaningful novelty in action. The technical scope of the role includes identifying and advancing synthetic data generation approaches that more richly embody noisy, irregularly sampled, sparse, multi-dimensional geospatial data.


  • Design and implement cutting edge AI and mathematical techniques to represent real world environments in the electro-optical and infrared domains, to include multi-sensor fusion approaches for multi-modal streaming data.
  • Develop a synthetic data environment for EO/IR data using a gaming engine such as unity, or similar, suitable for interacting with reinforcement learning libraries.
  • Compare and improve the performance of AI approaches to the detection and identification of entities in EO and IR data and investigate novel approaches e.g. the application of rough path theory for streaming data.
  • Develop AI approaches for the detection of anomalies and outliers in electro-optical and infrared data.
  • Deliver robust transparent algorithms that can be reproducibly deployed at scale and develop models that are robust to transferral between domains.
  • Publish as lead or co-author in high impact peer reviewed journals and participate in and present at international conferences.
  • Adapt research directions and modify implementations to meet stakeholder requirements
  • Catalyse connections and collaborations across a distributed inter-disciplinary team through regular meetings, co-development of code, maintenance of shared gitlab repositories, and contribute to the research aims and challenges of the ARC-D centre.
  • Communicate technical concepts and disseminate research progress to the scientific community, colleagues and external partners.


  • A PhD in a relevant area, which will include Mathematics, Data science, Physics, Engineering, Computer Science, or related discipline.
  • Good background in one or more of the following areas: reinforcement learning, adversarial AI, AI approaches to outlier and anomaly detection, rough path theory, topological data analysis, graph signal processing, infrared and electroptical modelling, synthetic data generation environments eg. unity
  • Strong quantitative background, and preferably a strong machine learning and statistics background
  • Strong background in one or more of the following areas: Topological data analysis, Rough path theory, multi-agent reinforcement learning, adversarial AI, synthetic data generation.
  • Experience developing software in a scientific computing context, ideally in Python, including the use of established libraries such as NumPy, Tensorflow, PyTorch, and RL specific frameworks such as Ray/RLLib, Stable Baselines. Experience in development suites, systems and versioning products (e.g., Git, IDEs, Linux).
  • Experience with ML packages such as TensorFlow, PyTorch, Keras, Python (scikit-learn, pandas, numpy, scipy, matplotlib)
  • Multi-disciplinary and comprehensive knowledge of the design and implementation of AI architectures

Other information


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 3970 2148 or 0203 862 3340, or email

CLOSING DATE FOR APPLICATIONS: Sunday 19 February 2023 at 23:59

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This full time post is offered on a 2 year fixed term basis. The annual salary is £40,850 - £46,200 plus excellent benefits, including flexible working and family friendly policies,

Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant at a salary of £38,236 per annum

We are happy to consider an external secondment from partner organisations.

The Alan Turing Institute is based at the British Library, in the heart of London’s Knowledge Quarter.

We are currently assessing the results of our hybrid working trial, which ran for six months. We will soon publish a long-term workplace policy: as a guide, we anticipate this will be between 2-4 days per month. Some roles may require the jobholder to spend a greater number of days in the office, but the hiring manager will be able to confirm this during the interview.


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.

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

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

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 to find out how we can assist you.