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Robotics and Autonomous Systems● OpenPreview

PhD in AI-Enabled UWB Indoor Positioning for Industrial Robotics

Develop AI-driven UWB localization systems for autonomous industrial robots and smart factories.

Best for:Best for engineering graduates with expertise in robotics, AI, sensor fusion, and UWB technology seeking a research-focused PhD in industrial automation.

Technological University Dublin · Dublin, Ireland · R1 · First-stage researcher
More professors at Technological University Dublin
AI-enhanced localization algorithmsUWB technology for indoor positioningSensor fusion in roboticsIndustrial IoT and autonomous navigationDigital Twin technologiesIndustry 5.0 applications
Deadline
Dec 24, 2026
70 days
Funding
Unfunded
Self-funded (fees and materials to be paid by the student)
Start date
flexible (expected to begin after admission)
Temporary
Location
Dublin
Ireland

Overview

This PhD project at Technological University Dublin focuses on advancing indoor positioning technology for autonomous mobile robots and automated guided vehicles in industrial settings. The research will leverage Ultra-Wideband (UWB) technology combined with AI and sensor fusion (IMUs, cameras, LiDAR) to enhance localization accuracy, reliability, and real-time navigation in dynamic environments. The project explores AI-enhanced algorithms, adaptive navigation strategies, and potential applications of Digital Twin technologies for system testing and optimization. Aligned with Industry 5.0 priorities, the work aims to support intelligent, human-centric manufacturing systems across smart factories, logistics, and healthcare facilities. Successful candidates will contribute to high-impact publications and collaborate with industrial partners.

AI-driven roboticsUWB positioningIndoor localizationIndustrial automationSensor fusionAutonomous mobile robotsDigital TwinIndustry 5.0

What you'll do

  • Develop AI-based algorithms for UWB localization and sensor fusion to improve indoor positioning accuracy in industrial environments.
  • Design and implement adaptive navigation strategies for autonomous mobile robots in dynamic settings with obstacles and moving personnel.
  • Explore Digital Twin technologies for virtual representation and simulation of industrial environments.
  • Conduct research on intelligent path planning techniques for real-time navigation in complex factory layouts.
  • Collaborate with industrial partners to translate research findings into practical applications for smart factories and logistics.
  • Publish findings in high-quality academic journals and conference proceedings.

Requirements

Degree required: Bachelor's degree or equivalent in Engineering, Computer Science, Robotics, or a related field

Degree offered: PhD

Language: English; good proficiency (equivalent to IELTS 6.5 or TOEFL 90 if applicable)

Instruction: English

Required skills
  • Proven expertise in robotics or autonomous systems
  • Strong background in AI and machine learning techniques
  • Experience with sensor fusion (e.g., IMUs, cameras, LiDAR)
  • Familiarity with UWB technology or wireless positioning systems
  • Programming skills in Python, C++, or similar languages
Nice to have
Experience with Digital Twin technologies or simulation toolsKnowledge of Industry 5.0 or smart manufacturing systemsBackground in industrial IoT or real-time embedded systems
Eligibility
  • No nationality restrictions mentioned
  • No residency or age limits specified
  • No prior experience caps mentioned

Funding & terms

Type
Unfunded
Amount
Self-funded (fees and materials to be paid by the student)
Duration
—
Programme
Student self-funded
Student self-funded

How to apply

Deadline: 2026-12-24 (Europe/Dublin)

Applicants must submit their application through the provided external form. Ensure your CV, a research statement, and proof of English proficiency (if required) are included. The application process is competitive, and selection will be based on academic merit and research potential.

Documents required
  • CV
  • Research statement or proposal
  • Proof of Bachelor's degree or equivalent
  • Proof of English proficiency (if applicable)
↗Apply via form

Contact

phd@tudublin.ie

http://www.tudublin.ie/phd

Lab
Directorate of Research, Enterprise & Innovation Services
Technological University Dublin
Ireland
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