Data Science Institute

We aim to set the global standard for a truly interdisciplinary approach to contemporary data-driven research challenges. Established in 2015, the Data Science Institute (DSI) has over 300 members and has raised £50 million in research grants.

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10-year anniversary of DSI – “Decade of Data Science”

In 2025, the Data Science Institute (DSI) at Lancaster University proudly marks its 10th anniversary. Since its founding in 2015, the DSI has established itself as a leading hub for cutting-edge research, interdisciplinary collaboration, and real-world impact in data science and artificial intelligence. Over the past decade, our researchers and partners have tackled some of the most pressing challenges in society, science, and industry—advancing the foundations of data science, fostering ethical and trustworthy AI, driving innovation across sectors and training 100s of data science practitioners.

As we celebrate this milestone, we reflect on the achievements of our vibrant research community and the transformative projects that have shaped the field. Looking ahead, the DSI remains committed to pushing the boundaries of data science and AI research, strengthening global collaborations, and supporting the next generation of data scientists.

About us

We are working to create a world-class Data Science Institute at Lancaster (DSI@Lancaster) that sets the global standard for a truly interdisciplinary approach to contemporary data-driven research challenges. DSI@Lancaster aims to have an internationally recognised and distinctive strength in being able to provide an end-to-end interdisciplinary research capability - from infrastructure and fundamentals through to globally relevant problem domains and the social, legal and ethical issues raised by the use of Data Science.

The Institute is initially focusing on the fundamentals of Data Science including security and privacy together with cross-cutting theme areas consisting of environment, resilience and sustainability;health and ageing, data and society and creating a world-leading institute with over 300 affiliated academics, researchers, and students.

Our data science, health data science and business analytics programmes have launched the careers of hundreds of data professionals over the last 10 years. Students from our programmes have progressed to data science roles at Amazon, PWC, Ernst & Young, Hawaiian Airlines, eBay, Zurich Insurance, the Co-operative Group, N Brown, the NHS and many others - please look at our Education pages for further details of the courses on offer.

Decade of Data motif

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EPSRC Doctoral Landscape Award (DLA) - PhD opportunity

Fighting fit – Optimising return to duty for UK military personnel using mobile gait analysis

Optimising recovery and return to combat readiness is key to ensuring military personnel are prepared for deployment in the UK and overseas. Utilising 3D motion capture for assessing gait of military personnel is the “gold standard,” but not logistically feasible due to the number of personnel requiring assessment, time taken to assess, digitise, and analyse the data, cost, and expertise to interpret it.

AI driven mobile phone applications that track gait have the potential to revolutionise how we assess gait and diagnose gait disorders not only in military personnel but all clinical populations. Their ease of use, speed of feedback and low cost makes them an ideal tool to be implemented into clinical practice, particularly in a rehabilitation setting.

We have developed a 3D human estimation pose model capable of capturing and measuring gait, but it has not been validated in a clinical population or been compared to the “gold standard” 3D motion capture. Our aim in collaboration with the Ministry of Defence is to adapt and refine our existing 3D human estimation pose model to be able to automatically detect and diagnose gait disorders in military personnel with overuse injuries.

We have four key objectives:

Objective 1: To adapt our existing model to capture and analyse biomechanical data from military personnel with and without overuse injuries

Objective 2: To determine the validity of the biomechanical data obtained from our model compared to the “gold standard” 3D motion capture in military personnel with and without overuse injuries

Objective 3: To identify, test, and validate possible solutions for the modelto diagnose gait disorders in military personnel with overuse injuries

Objective 4: Test real time execution of digitisation, application and data extraction when using the model to diagnose gait disorders in military personnel with overuse injuries

We are looking for an enthusiastic, proactive and highly motivated PhD candidate.

Experience in 3D motion capture, machine learning, AI or data analysis strongly desirable. This project is in collaboration with the Ministry of Defence, and some travel will be expected between Lancaster University (host institution) and the Defence Medical Rehabilitation Centre Stanford Hall for meetings, recruitment of personnel and data collection.

Essential:

  • 2:1 or 1st class undergraduate degree (or equivalent) in sport science, biomechanics, computer vision or computer science related disciplines

Strongly desirable:

  • A Merit or Distinction postgraduate degree (or equivalent experience) in sport science, biomechanics, computer vision or computer science related disciplines
  • Experience in collecting data from participants in research studies
  • Demonstrate expertise in quantitative research methods
  • Experience in machine learning and/or AI
  • Experience in 3D motion capture and/or biomechanical assessment of gait
  • Experience of presenting at international conferences and/or publishing in peer-reviewed journals

Funding:

A successful applicant will receive a stipend towards living expenses at the UKRI rate (currently £ £20,780 per year) and £1000 per year to support training and development needs (e.g., attend courses or conferences).

Supervisory team:

Dr Hannah Jarvis - Lancaster University has expertise in gait assessment and led previous research projects with the Ministry of Defence. Co-supervisors are Professor Jun Liu - Lancaster University, expertise in computer vision and machine learning, and Professor Neil Reeves - Lancaster University, expertise in digital health technologies and cyber security. You will also be supervised by colleagues from DMRC Stanford Hall.

In your application, please include:

  • A cover letter detailing why you are the most appropriate person for the position
  • A CV

In your application, please include:

  • A cover letter detailing why you are the most appropriate person for the position
  • A CV

Application deadline 31st October 5pm

Interviews early November – date tbc

Contact Information:

Please contact Dr Hannah Jarvis (H.Jarvis@lancaster.ac.uk)

EPSRC Doctoral Landscape Award (DLA) - PhD opportunity

Data-Driven Hybrid Motion–Force Control for Robust Human–Manipulator InteractionLancaster University – in collaboration with United Kingdom National Nuclear Laboratory (UKNNL)

We invite applications for a fully funded PhD studentship at Lancaster University’s School of Engineering, in partnership with United Kingdom National Nuclear Laboratory (UKNNL). This exciting project will develop novel data-driven, robust, and adaptive control methods for human–robot interaction and teleoperation, with direct applications in nuclear robotics, hazardous environment manipulation, and beyond.

Project Overview

Teleoperation is a critical enabler for safe and efficient operation in hazardous environments such as nuclear decommissioning. However, current industrial solutions suffer from limitations under uncertainty, time delays, and noisy sensing.

This PhD project will design and experimentally validate a hybrid motion–force control framework that ensures precise end-effector positioning while maintaining robust and adaptive force regulation under real-world conditions. Research will include:

  • Development of nonlinear robust adaptive controllers and disturbance observers.
  • Design of bilateral teleoperation schemes that enhance transparency and stability under communication delays.
  • Integration of data-driven approaches for force estimation and safety.
  • Experimental validation on industrial robotic platforms at the UKNNL Hot Robotics Facility.

The project provides the opportunity to work on cutting-edge robotics challenges with significant industrial impact, supported by state-of-the-art facilities at both Lancaster University and UKNNL.

Supervisory Team

  • Dr Allahyar Montazeri (Lead Supervisor, School of Engineering, Lancaster University; Data Science Institute Member)
  • Prof Plamen Angelov (Co-Supervisor, School of Computing and Communications, Lancaster University; Data Science Institute Member)
  • Dr Naomi Rutledge Industrial co-supervisor

Training and Development

The successful candidate will receive a tailored training programme including:

  • Hands-on training with ROS2, MATLAB/Simulink, and CoppeliaSim.
  • Access to world-class robotics laboratories and facilities.
  • Opportunities to engage with national and international conferences, workshops, and training events.
  • Insight into the nuclear sector through industrial collaboration with UKNNL.

Funding

  • Duration: 4 years (3.5 years EPSRC Doctoral Landscape Award + 0.5 years UKNNL extension)
  • Coverage: UKRI minimum stipend, tuition fees for Home students, and a research training support grant.
  • Additional support for consumables, maintenance, and travel.

Eligibility

  • Open to UK Home students only, due to clearance requirements for UKNNL facilities.
  • Applicants should have (or expect to obtain) a First or Upper Second-Class degree (or equivalent) in Engineering, Control, Robotics, Computer Science, or a related discipline.
  • Strong mathematical and programming skills (MATLAB, Python, or C++) are highly desirable.

Application Process

Applicants should submit:

  1. A full CV.
  2. A one-page cover letter outlining their motivation and suitability for the project.
  3. Reference letter from two academics commenting on the candidate abilities.

Applications will be considered on a rolling basis until the position is filled, with an expected start date of January 2026.

For informal enquiries, please contact Dr Allahyar Montazeri (a.montazeri@lancaster.ac.uk).

Application deadline 1st December

DSI Workshop Call Out 2025

Apply for funding for interdisciplinary research workshops

The Data Science Institute supports interdisciplinary research across a wide range of disciplines and interests. Institute members are passionately engaged in a wide range of data-intensive activity, which is addressing critical societal, economic and environmental questions, as well as generating new mathematical models and methods of digital innovation. We are delighted to collaborate with a wide range of centres across the University and take a broad definition of ‘data’ and ‘science’ - reflecting an inclusive outlook, which is critical to our mission.

To support the development of cross-disciplinary data intensive activity at Lancaster we are offering workshop funding for your projects. We welcome applications from colleagues with interests, which may include: the foundations of data science and AI, environmental data science, health data science, AI for design, AI and society, data and inclusion, digital innovation, citizen science and data visualisation. If you are unsure your proposal fits – please do drop a line to DSI.

Your workshops should aim to bring colleagues together for exciting dialogue and exchange, you may want to invite external colleagues from other universities, industry, policy, charity or civil society. Our only stipulation is that you must demonstrate collaboration beyond a single discipline.

Further Information. Proposals are sought for workshops to be held at Lancaster before the end of the academic year 2025/26. Funding of between £2000 - £5000 is available for each workshop. Funds can be requested to cover travel and subsistence costs associated with inviting UK/international speakers to the workshop, together with local costs associated with the event itself (venue, refreshments & food, etc.). We encourage workshops to invite non-LU researchers to attend and so it may be appropriate to charge external folks a nominal workshop fee to cover some of the costs, if your budget will exceed £5000.

DSI will be able to provide administrative support to help with these workshops (e.g., setting up the financial aspects, rooms and catering bookings) however the proposer(s) and a local organising committee will be ultimately responsible for organising the workshop. You will be asked to provide a short report for the DSI website of not more than 2-3 paragraphs, following the conclusion of your workshop. This may include photographic material where appropriate permissions are sought.

Proposals will be reviewed by a sub-group of DSI leadership and members, taking into account any conflicts of interest. We will aim to ensure, as far as possible, that funding is spread across the faculties of the University.

Proposal format

  • Proposals should consist of the following
  • Proposers: (inc. departmental affiliation)
  • Workshop focus: brief description of aims
  • Proposed local organisers (inc. departmental affiliation)
  • List of potential invited speakers
  • Brief justification of funds requested
  • Expected outcome/benefits from the workshop
  • Proposals should be sent in pdf/word format to the DSI mailbox by 31st October 2025
  • Please keep to two sides of A4

We look forward to reading your proposals!

Should you have any questions, please do not hesitate to contact DSI.

Data Dialogues - Autumn 2025

We would like your suggestions for speakers for Autumn 2025 - please get in touch if you would like to present or have a nomination to make!

Data Dialogues is an informal, discussion-driven event where members of the DSI and the broader university community share insights into their work, spark interdisciplinary conversations and explore potential collaborations. The focus is on interactive engagement rather than formal presentations—so no slides (or just a few, if needed)! Instead, the idea is to introduce your work in an accessible way, followed by an open discussion and Q&A with attendees.

Get fresh perspectives and think about new ways of approaching your own research, meet new people and explore potential research collaborations. Come be part of the DSI community!

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Events

Björn Andersson is a professor at the Centre for Educational Measurement, University of Oslo (CEMO), Norway - 16th October at 2.15pm

Title: Joint latent variable modelling of binary, ordinal, count, and continuous data for social science and psychological research

Abstract: Research in the social sciences increasingly utilizes mixed data types, such as a combination of ordinal item scores, continuous response times, and discrete variables based on the response process. Generalized linear latent variable models (GLLVMs) fitted to such data can be used to infer relationships between multiple latent constructs and their development over time. These models are often high-dimensional and require efficient estimation methods. We propose an estimator for GLLVMs based on maximizing an approximation to the marginal likelihood and discuss the finite sample and large sample properties of the estimator. The modelling approach is illustrated through joint modelling of response times, action counts, and item scores, where we examine the impact on measurement precision of the proficiency estimates when including multiple types of variables. Extensions of the modelling approach to longitudinal data analysis and joint modelling with non-ignorable missing data are also discussed.

16th October at 2pm in the Post Grad Stats Centre Lecture Theatre at 2.15 - 3.45pm

Sign up via Eventbrite

Biography

Björn Andersson is a professor at the Centre for Educational Measurement, University of Oslo (CEMO), Norway. He obtained his Ph.D. in statistics from Uppsala University in 2014 and has worked as a post-doctoral researcher (2015-2017) at the Collaborative Innovation Center of Assessment towards Basic Education Quality, Beijing Normal University in Beijing, China. His research interests include estimation methods for latent variable models, methods to ensure comparability of test scores in applied measurement and applications of item response theory in education, mental health, and psycholog

Introduction to R - Wed 22nd October from 10 am – 4:30 pm

Date/Time: Wed 22nd Oct from 10 am – 4:30 pm, with a lunch (and LEC seminar) break from 12:30 pm to 2:30 pm.

Description: This workshop will teach basic skills for using R for data analysis. We assume no prior knowledge of R or general coding experience, although people with previous experience can also attend. PGR students from LEC and BLS will be prioritized for attendance. Other staff and students can request to attend and will be notified if there is space available.

Please sign up via Eventbrite and let us know your department and career stage you are at through the sign up process.

Unlocking the power of Data Science and AI for environmental research - 24th October, 11am - Sky Lounge

Date: 24th October

Location: Sky Lounge, Infolab 21, Lancaster University

Time: 11:00-14:00

Free event plus pizza lunch!

Are you an early career researcher (PhDs, postdocs and more) at Lancaster Uni or UKCEH working in ecology, environmental science or conservation? Whether you’re already using data science (includes AI and machine learning) or eager to explore new ways it can advance your research, this interactive session is for you.

Join us to:

  • Collaborate on a real-world environmental data challenge in a friendly, engaging environment.
  • Explore fresh approaches and tools to elevate your data skills.
  • Connect with fellow PhDs, postdocs and ECRs from across disciplines.
  • Learn about the Data Science Institute (DSI)and the Centre of Excellence for Environmental Data Science (CEEDS)* at Lancaster University — vibrant communities offering:
    • Access to seminars, workshops, and networking events
    • Funding opportunities and collaborative projects
    • Training and support tailored to environmental data science

Whether your focus is on the biodiversity of micro-organisms or global atmospheric dynamics, this event will inspire new ideas and connections.

Whatever your current level of data science expertise, come join us and see how DSI and CEEDS can support your research journey! If nothing else, you get free pizza.

* CEEDS is a joint centre that aims to build collaboration between UKCEH and Lancaster University. Funding is available via CEEDS to support travel for UKCEH participants not based at Lancaster, and will be allocated on a first-come, first-served basis.

Reserve your spot now

Questions? Contact Julia Carradus j.carradus1@lancaster.ac.uk

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Research Themes

Data Science at Lancaster was founded in 2015 on Lancaster’s historic research strengths in Computer Science, Statistics and Operational Research. The environment is further enriched by a broad community of data-driven researchers in a variety of other disciplines including the environmental sciences, health and medicine, sociology and the creative arts.

  • Foundations

    Foundations research sits at the interface of methods and application: with an aim to develop novel methodology inspired by the real-world challenge. These could be studies about the transportation of people, goods & services, energy consumption and the impact of changes to global weather patterns.

  • Health

    The Health theme has a wide scope. Current areas of strength include spatial and spatiotemporal methods in global public health, design and analysis of clinical trials, epidemic forecasting and demographic modelling, health informatics and genetics.

  • Society

    Data Science has brought new approaches to understanding long-standing social problems concerning energy use, climate change, crime, migration, the knowledge economy, ecologies of media, design and communication in everyday life, or the distribution of wealth in financialised economies.

  • Environment

    The focus of the environment theme has been to seek methodological innovations that can transform our understanding and management of the natural environment. Data Science will help us understand how the environment has evolved to its current state and how it might change in the future.

  • Data Engineering

    The Data Engineering theme aims to explore how we can utilise digital technologies to accelerate and enhance our research processes across the University.

Research Software Engineering

Within the Data Science Institute, our aim is to improve the reproducibility and replicability of research by improving the reusability, sustainability and quality of research software developed across the University. We are currently funded by the N8CIR, and work closely with our partner institutions across N8 Research.

Research Software Engineering

Upcoming Events

On
Now

Ecology and AI Workshop - 13th Oct

Monday 13 October 2025, 9:30am to 4:30pm
15th Oct

Health@Lancaster Collaboration Cafe - Rob Lyon of Advanced Oxygen Therapy, Inc. and Professor Neil Reeves

Wednesday 15 October 2025, 9:00am to 10:00am
15th Oct

Environmental Digital Humanities Seminar: Artemis - Advanced Research Tools for Environmental Studies for Historical Maps of the Scheldt Valley

Wednesday 15 October 2025, 12:00pm to 1:00pm
16th Oct

Professor Björn Andersson Centre for Educational Measurement, University of Oslo

Thursday 16 October 2025, 2:15pm to 3:45pm
22nd Oct

DSI - Introduction to R

Wednesday 22 October 2025, 10:00am to 4:30pm

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Applications open: Society Theme Lead, Data Science Institute (DSI)

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The Data Science Institute invites applications for the role of Society Theme Lead. Working closely with the DSI Directors and Theme Lead cohort, the post-holder will shape and deliver the Institute’s mission at the intersection of data/AI and society.

Eligibility
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