PhD Project: AI-Enabled UWB Indoor Positioning for Industrial Robotics

EURAXESS Ireland; Dublin; On-site; EUR 18,000 - 25,000; Full time

Job summary

EURAXESS Ireland is offering a PhD position focused on developing AI-based autonomous mobile robotics solutions using Ultra-Wideband (UWB) technology. This research aims to overcome indoor positioning challenges in various sectors, including manufacturing and logistics.

Candidates should possess a relevant degree in Electronic Engineering or a related field and show a strong interest in robotics, AI, and smart manufacturing. The project involves collaboration with industry partners and emphasizes publication in high-quality journals.

Qualifications

• Minimum Bachelor’s or Master’s degree in Electronic Engineering, Robotics, or closely related discipline.
• Strong analytical and problem-solving skills.
• Good written and verbal communication skills in English.

Responsibilities

• Develop intelligent UWB-based positioning systems for industrial robotics.
• Research AI-enhanced localization algorithms and adaptive navigation strategies.
• Collaborate with industrial partners and research networks.

Skills

Robotics and Autonomous Systems

Artificial Intelligence and Machine Learning

Wireless Communication Systems

Ultra-Wideband (UWB) Technologies

Education

Bachelor’s or Master’s degree in Electronic Engineering or related field

Tools

Python

MATLAB

C/C++

Job description

Directorate of Research, Enterprise & Innovation Services

Organisation/Company Technological University Dublin Department Directorate of Research, Enterprise & Innovation Services Research Field Engineering “Electronic engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 24 Dec 2026 - 17:25 (Europe/Dublin) Country Ireland Type of Contract Temporary Job Status Negotiable Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

The growing adoption of autonomous mobile robots (AMRs) and automated guided vehicles (AGVs) is transforming modern manufacturing, warehousing, logistics, and smart factory environments. Accurate indoor positioning remains a critical challenge for these systems, as traditional satellite-based navigation technologies such as GPS are ineffective in indoor industrial settings. Ultra-Wideband (UWB) technology has emerged as a promising solution due to its high positioning accuracy, low latency, and robustness in complex environments.

This PhD project aims to develop intelligent UWB-based positioning and navigation systems for industrial robotics using Artificial Intelligence (AI) and advanced sensor fusion techniques. The research will investigate how UWB measurements can be combined with data from onboard sensors such as inertial measurement units (IMUs), cameras, and LiDAR to improve localization accuracy, reliability, and real-time navigation performance in dynamic industrial environments.

The project will focus on the design of AI-enhanced localization algorithms, intelligent path planning techniques, and adaptive navigation strategies capable of operating in environments with obstacles, moving personnel, and changing factory layouts. Digital Twin technologies may also be explored to create virtual representations of industrial environments for system testing, optimisation, and performance evaluation.

The research aligns with Industry 5.0 priorities, supporting the development of intelligent, connected, and human‑centric manufacturing systems. Potential application areas include smart factories, warehouse automation, logistics, healthcare facilities, and collaborative robotics.

The successful candidate will gain expertise in UWB systems, robotics, Artificial Intelligence, Industrial IoT, sensor fusion, and autonomous navigation. The project is expected to generate multiple high‑quality journal and conference publications and provide opportunities for collaboration with industrial partners and international research networks.

Self Funded (Scholarship not available. Fees & Materials to be paid by the student.)

Applicants should hold a minimum of a Bachelor’s degree or a Master’s degree in Electronic Engineering, Electrical Engineering, Robotics, Mechatronics, Automation Engineering, Computer Engineering, Computer Science, Telecommunications Engineering, or a closely related discipline.

The ideal candidate will have an interest in one or more of the following areas:

• Robotics and Autonomous Systems
• Artificial Intelligence and Machine Learning
• Wireless Communication Systems
• Ultra-Wideband (UWB) Technologies
• Industrial Automation and Industry 5.0
• Industrial Internet of Things (IIoT)
• Sensor Fusion and Localization
• Embedded Systems and Edge Computing
• Computer Vision and Intelligent Navigation

Experience in one or more of the following would be advantageous but is not essential:

• Programming using Python, MATLAB, C/C++, or ROS (Robot Operating System)
• Mobile robotics or autonomous systems
• Wireless communication and positioning systems
• IoT devices and sensor networks
• Machine learning and data analytics
• FPGA, embedded platforms, or real‑time systems

Specific Requirements

• Strong analytical and problem‑solving skills.
• A passion for emerging technologies in robotics, AI, and smart manufacturing.
• The ability to work independently and as part of a multidisciplinary research team.
• Good written and verbal communication skills in English.
• Motivation to publish research findings in high‑quality international journals and conferences.

This project is particularly suitable for candidates seeking careers in robotics, intelligent automation, industrial AI, autonomous systems, smart manufacturing, logistics automation, and next‑generation industrial communication systems. The interdisciplinary nature of the project provides opportunities to develop expertise across robotics, wireless sensing, Artificial Intelligence, and Industrial IoT technologies that are highly sought after by both academia and industry.