[School of Engineering PhD Scholarships] Exploring Novel and Unfamiliar Data Visualisations

About the project

About the Project Each year, research explores new ways to visualise data. For example, correlation, which is traditionally visualised using bivariate scatterplots, can also be visualised using donut charts or stripplots (Harrison et al., 2014), and there are a number of ways to visualise the structure of quantitative data, such as histograms, raincloud plots (Allen et al., 2021), or violin plots. Simultaneously, alterations to traditional visualisation techniques are being studied with the intent of finding new and better ways to visualise data and the associated foundational statistical concepts. Examples of these modifications include changing the size or opacity of scatterplot points (Strain et al., 2023; Strain et al., 2023b; Strain et al., 2024), changing the levels of smoothing employed in line graphs (Moritz et al., 2024), or using different sizes of bars on bar charts (Talbot et al., 2014). These novel visualisations and techniques are often investigated with the goal of improving an aspect of people’s interactions with them. Perceptual accuracy, cognitive integration, ease-of-use, and satisfaction are qualities that are designed for in visualisation studies each year. Yet, what are the subjective judgements of everyday users of visualisations when confronted with a novel, unfamiliar data visualisation? While lay participants provide a vast amount of (good quality) data that informs visualisation design, it is unlikely that any of them ever see the final designs their data inform. The scope of this PhD project is open-ended, and we hope that the successful candidate will bring their perspective to a project they feel ownership over. Some questions that might be worth a thought: Does unfamiliarity and novelty in data visualisation affect how much participants trust visualisations? Are there trade-offs that have not been considered with regards to the benefits derived from novel visualisation techniques? Can existing visualisation education, such as that covered in a GCSE or A-level mathematics course, inform the development of visualisation techniques such that any negative effects of unfamiliarity are assuaged? The research team is committed to producing high-quality work that is intended for publication in high impact venues. The team is also committed to carrying out work in an open and reproducible manner; engagement with this is expected of the successful candidate. This project is expected to start in September 2027. Before you apply: We strongly recommend that you contact the supervisors for this project before you apply. How to apply: To be considered for this project you must complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for PhD School of Engineering Scholarships . If you don’t already have an applicant account, please follow the instructions here. . When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing the motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications . Please note late or incomplete applications will not be considered. Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status. We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder. Eligibility : The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree (or international equivalent) in a subject related to Human-Computer Interaction, such as Computer Science or Psychology OR any upper-second class (2:1) honours degree and a Master’s degree at merit (or international equivalent) in a subject related to Human-Computer Interaction, such as Computer Science or Psychology. Some coding experience in R or Python required. An appreciation of general issues in HCI, experimental design, and data visualisation preferred. This project will remain open until filled. If your application is submitted by 1 st November 2026, you can expect a decision by 18 th December 2026. If your application is submitted by 15 th January 2027, you can expect a decision by 30 th March 2027. Self or externally funded students can also be considered for this project. FSESoE

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