Open PhD Positions (2 EULA PHD Positions + 1 EPSRC CDT Position)

We are currently recruiting PhD students for fully funded research opportunities within our lab and collaborating UCL departments. Below are details for two positions funded under the UCL EPSRC Doctoral Landscape Award (UELA), and one position funded under the EPSRC CDT in Cyber-Physical Risk.

Important Funding and Application Guidelines (UELA 2027–28)

  • Funding and Value: Fully funded 4-year PhD studentship (covers Home tuition fees, research costs/RTSG, and a living stipend estimated at ~£26,022/year).
  • Eligibility: Home Fee Status candidates only (as designated in the project specifications).
  • Application Deadline: 8 January 2027 at 12:00 noon (UK Time).

    Note: UCL is closed over the Christmas break with no email support available. Applicants are strongly advised to submit their application and ensure referee responses are received by 16 December 2026.

  • General UELA Information: UCL EPSRC Doctoral Landscape Award (UELA) Overview
  • Application Guidance PDF: Download UELA Applicant Guidance

Available UELA PhD Projects

Project 1: Real-Time In Situ Acoustic Mapping of Aquatic Microplastics via Ultrasonic Wavefront Reconstruction and Edge Machine Learning

  • Project ID: 2531cs1045
  • EPSRC Theme: Artificial Intelligence and Robotics
  • Lead Department: Department of Computer Science
  • Eligibility: Home fee status candidates only
  • Supervisory Team: Dr. Sajad Saeedi, Dr. Katherine Curran, Prof. Simon Julier, Dr. Valerio Modugno
  • Inquiries: s.saeedi@ucl.ac.uk

Project Overview

Microplastic pollution in riverine and marine ecosystems represents a major ecological threat, yet existing monitoring relies heavily on manual sampling and lab-based spectroscopy—methods incapable of continuous spatial tracking. Building upon our foundational ultrasound microplastic detection breakthrough published in Nature (2026), this project advances beyond the state-of-the-art by moving from static, laboratory-controlled detection to real-time 3D wavefront reconstruction in turbulent, uncalibrated fluid environments with non-spherical and polydisperse microparticles.

The PhD candidate will address fundamental computational challenges across three core research pillars:

  1. Signal Processing and Acoustic Inversion: Formulating ultra-fast wave propagation and backscattering inversion algorithms to decode complex and multipath acoustic signatures from dynamic microparticles in fluid flows.
  2. Edge ML and On-Device Spatial Reconstruction: Designing ultra-lightweight and physics-informed neural network architectures optimised for low-power microcontroller payloads (e.g., deployed on AUVs or autonomous river buoys). Edge computing is strictly essential to overcome underwater/remote bandwidth limits, processing raw high-frequency acoustic streams on-site and transmitting only compressed spatial concentration heatmaps.
  3. Environmental Validation and Field Integration: Embedding these algorithmic models into field-ready sensing prototypes in collaboration with the UCL Bartlett School of Environment, Energy and Resources, validating in situ tracking across complex aquatic matrices.

Impact and Beneficiaries

By replacing slow laboratory spectroscopy with continuous in situ mapping, this work directly empowers environmental regulators (UK Environment Agency, Defra, Cefas) with actionable, real-time data for policy enforcement and pollution tracing. Furthermore, open-source edge ML algorithms will equip regional water utilities (Thames Water) and marine instrumentation leaders (Sonardyne International, Chelsea Technologies) with next-generation contamination tracking capabilities while advancing fundamental computational methodologies in acoustic physics-informed AI.

Project 2: Distributed Multi-Robot Cooperative Transport for Autonomous On-Site Construction

  • Project ID: 2531cs1006
  • EPSRC Theme: Artificial Intelligence and Robotics
  • Lead Department: Bartlett School of Architecture (BSA) / Department of Computer Science
  • Eligibility: Home fee status candidates only
  • Supervisory Team: Prof. Sean Hanna, Dr. Sajad Saeedi, Prof. Simon Julier, Pradeep Devadass
  • Inquiries: s.saeedi@ucl.ac.uk

Project Overview

Automating logistics and material assembly on construction sites is critical for addressing labour shortages, reducing material waste, and improving operational safety. While single autonomous mobile robots (AMRs) excel at point-to-point delivery of standardised payloads, modern construction relies on large, heavy, or geometrically irregular components (e.g., structural timber beams, prefabricated facade panels) that exceed the payload and manoeuvrability limits of individual agents.

This interdisciplinary project will develop a scalable computational framework for distributed multi-robot cooperative transport tailored to unstructured and dynamic construction environments.

The PhD candidate will address three core algorithmic and systems-level challenges:

  1. Decentralised Multi-Agent Coordination: Designing resilient low-latency consensus protocols and learning-based control algorithms that enable teams of heterogeneous robots (AMRs/quadrupeds) to dynamically share heavy physical loads under intermittent communication without centralised coordination.
  2. Force-Aware Cooperative Control: Developing real-time control architectures that exploit tactile and torque feedback across payload contact points, allowing robots to implicitly communicate through physical load dynamics.
  3. Dynamic Motion Planning in Unstructured Terrains: Formulating distributed collision-avoidance algorithms for unstructured terrains alongside autonomous fault-recovery protocols, ensuring the team can redistribute weight or execute a safe emergency stop during agent failure to prevent physical damage.

Impact & Beneficiaries

  1. Construction and AEC Industry: Enables safe and automated handling of heavy structural elements on active jobsites, reducing human workplace injury and accelerating construction timelines. Target partners include UK construction leaders (Laing O’Rourke/Mace), digital architecture groups (Foster + Partners), and safety regulators (Construction Innovation Hub).
  2. Robotics and Automation Sector: Delivers open-source toolkits applicable beyond construction to industrial warehousing, disaster response, and logistics.
  3. Environmental Policy Makers: Demonstrates pathways toward lower-carbon, efficient automated building sites, reducing material damage and energy footprint (HSE, UKRI Green Construction).

Available EPSRC CDT in Cyber-Physical Risk Project

Project 3: 4D Object-Oriented Crime Scene Reconstruction and Simulation using Neural Scene Models

  • Project Program: EPSRC Centre for Doctoral Training (CDT) in Cyber-Physical Risk
  • Lead Department: Department of Computer Science / Security and Crime Science
  • Eligibility: Home fee status candidates only
  • Research Themes: Simulation and Interaction
  • Supervisory Team: Dr. Sajad Saeedi (UCL Computer Science) and Prof. Hervé Borrion (UCL Security and Crime Science)
  • Inquiries: s.saeedi@ucl.ac.uk or scs.phd@ucl.ac.uk

Project Overview

Understanding, reconstructing, and analysing crime scenes requires integrating evidence from both the physical and digital domains. This project proposes to develop a 4D (3D + time) object-oriented mapping framework that fuses multi-sensor data (e.g., images, LiDAR, videos, and digital traces) using advanced neural representations such as neural radiance fields and graph-based scene reasoning.

The resulting “living digital twin” of a crime scene will allow investigators to interactively explore spatio-temporal hypotheses, simulate human behaviour, and test physics-based object interactions under various scenarios. The system will enable users to examine causal relationships and reconstruct events consistent with available evidence.

Impact and Objectives

This research will help investigators manage cyber-physical risk by linking digital evidence (e.g., CCTV, IoT data) with physical actions, ultimately enhancing situational awareness, forensic reconstruction, prevention, and decision-making in complex hybrid crime scenes.

Key Application Details and CDT Guidelines

  • Application Deadline: 8 January 2027 at 12:00 noon (UK Time).

    Note: UCL is closed over the Christmas break with no email support available. Applicants are strongly advised to submit their application and ensure referee responses are received by 16 December 2026.

  • Funding and Value: Fully funded EPSRC CDT 4-year PhD studentship covering Home tuition fees and a stipend.
  • Eligibility: Candidates qualifying for UK Home Fee status.
  • Project Reference & Details: UCL EPSRC CDT in Cyber-Physical Risk Project Proposals
  • How to Apply: Applicants should explore the project details and contact the primary supervisor (s.saeedi@ucl.ac.uk) or the CDT administration team (scs.phd@ucl.ac.uk) directly to discuss suitability before submitting a formal application.