PhD Scholarship: Combining Semantic Web and Machine Learning for Representing and Manipulating Point Cloud Data

PhD Scholarship: Combining Semantic Web and Machine Learning for Representing and
Manipulating Point Cloud Data

University College Dublin invites applications for a fully-funded PhD scholarship to investigate the potential advantages of combining Semantic Web (SW) and Machine Learning (ML) for representing 3D point cloud datasets and improving the algorithms used to analyse them. The project aims to develop information representations that can enable humans and machines to understand, interpret, and reason about point cloud data and their associated analytical methods.

The successful candidate will join the School of Computer Science at UCD, ranked among the top in the world, and will be part of the Spatial Research group, a multidisciplinary community focused on cutting-edge research on Spatial Information Systems and GIScience. As part of the School and research group, the candidate will benefit from access to leading experts, collaborative networks, and career development opportunities.

University: University College Dublin

School: School of Computer Science

Primary Supervisor: Dr Anh Vu Vo, Assistant Professor, School of Computer Science, anhvu.vo@ucd.ie

Co-Supervisor: Prof Michela Bertolotto, Professor, School of Computer Science

Renumeration: This studentship will include:

4-year tax-free stipend (25,000 Euro annually)

Registration fees

A travel and equipment budget

The PhD candidate will do some demonstration and/or teaching assistant hours in the School as part of their career development. These teaching activities are an additional source of income, complementing the offered stipend.

Research Area:

3D point clouds have become an increasingly important type of geospatial data, widely used in areas such as urban planning, forestry, transportation, and environmental monitoring. Their popularity has surged in recent years as many countries across Europe, including Ireland, the Netherlands, and Denmark, have undertaken large scale LiDAR data acquisition projects. Similarly, in the United States, the US Geological Survey has recently completed its decade-long 3D Elevation Program, successfully mapping the entire country in 3D. These nationwide initiatives have generated massive volumes of point cloud data, much of which is freely available to the public.

In parallel, a growing number of algorithms and ML models for processing and analysing point clouds are being developed to attempt fo fully utilise the data. However, the sheer scale, heterogeneity, and distributed nature of these data and analytical resources pose serious challenges for effective management, discovery, and reuse. Key information about data provenance, algorithm behaviour, or appropriate usage contexts is often lost or inconsistently documented, making it difficult to integrate or reproduce workflows.

This PhD project will explore how SW technologies and ML can be combined to improve the organisation, interpretation, and reuse of point cloud datasets and analysis methods. SW resources, such as ontologies and knowledge graphs, can provide structured, human- and machine-readable descriptions of data and algorithms, supporting reasoning, discoverability, and interoperability. Meanwhile, ML offers scalable techniques for classifying, segmenting, and extracting patterns from point clouds and for learning representations of both data and workflows.

By integrating SW and ML, the project aims to develop novel methods for creating effective, reusable, and semantically rich representations of point cloud data and algorithms. The specific research direction will be shaped in collaboration with the PhD candidate, based on their interests and expertise.

Selection Criteria

The ideal candidate should hold an undergraduate or postgraduate degree in Computer Science, Engineering, Information Science or a related technical discipline. A strong background in computer programming and a genuine passion for research are essential. Familiarity with concepts in information science, such as data representation, metadata, knowledge organisation, or information retrieval, would be advantageous.

Candidates with prior research experience are especially encouraged to apply. Strong interpersonal skills and the ability to work both independently and collaboratively within a research team are important.

Excellent English communication skills are required. Non-native English speakers must provide evidence of proficiency through an IELTS score of at least 6.5 overall, with no component below 6.0, or an equivalent qualification. Applicants who have completed a degree in an English-speaking country are exempt from this requirement.

How to Apply:

Applications should be submitted by email to Dr Anh Vu Vo ((opens in a new window)anhvu.vo@ucd.ie). An application should include:

• A CV including relevant prior publications (if available)

• A cover letter (max 1000 words) detailing your interest in this research area and position
• The name and contact details of 2 academic referees
• A writing sample, such as a publication or chapter from a thesis
• A sample of your coding work

Shortlisted candidates will be contacted for an interview, expecting a commencement date in September 2025 or January 2026.