⛔ This position is no longer available.
University: University College Dublin
Country: Ireland
Deadline: 2026-04-25
Fields: Computer Science, Electrical Engineering, Data Science, Artificial Intelligence, Internet of Things
Are you passionate about shaping the future of intelligent, real-time systems and distributed machine learning for next-generation edge computing? If you are eager to work at the intersection of IoT, machine learning, and privacy-aware decision-making, this fully funded PhD position may be the ideal opportunity to advance your academic and professional journey.
About the University or Research Institute
University College Dublin (UCD) stands as one of Europe’s leading research-intensive universities, located in the vibrant capital of Ireland. UCD is internationally recognized for its commitment to innovation, interdisciplinary research, and fostering a diverse academic community. With a strong tradition of collaboration with industry and global partners, UCD provides its students with access to world-class facilities, expert supervision, and a dynamic research environment.
This PhD opportunity is offered in partnership with Pragma-IoT, an industry leader in IoT solutions based in Thessaloniki, Greece. The program is structured to provide an international experience, allowing the candidate to spend significant time at both UCD and Pragma-IoT, thereby gaining exposure to both academic and industrial research cultures. Ireland and Greece both offer rich cultural experiences, a high quality of life, and thriving technology sectors, making them attractive destinations for ambitious researchers.
Research Topic and Significance
The core focus of this PhD position is on “Edge-enabled Distributed Machine Learning with Multi-modal Data.” As the world becomes increasingly connected through IoT devices, there is a growing need for intelligent systems capable of making decentralized, real-time decisions while maintaining privacy and efficiency. The ability to process and analyze data at the edge—closer to where it is generated—reduces latency, enhances privacy, and enables smarter, faster responses in critical applications.
This research is particularly significant in the context of Connected and Cooperative Automated Mobility (CCAM) and future networked systems, where delay-sensitive decision-making can impact safety, efficiency, and user experience. By developing lightweight, distributed machine learning models that can operate on heterogeneous IoT devices, this project aims to push the boundaries of what is possible in edge computing and data fusion.
Project Details
The PhD candidate will engage in cutting-edge research to develop and implement distributed machine learning models tailored for edge devices. Key aspects of the project include:
– Building lightweight distributed machine learning models that address latency and privacy requirements.
– Integrating machine learning algorithms with digital twins (DTs) for continuous performance evaluation.
– Designing customized distributed models for heterogeneous IoT devices with varying computational capabilities and data modalities (such as video and audio).
– Developing multi-modal data fusion techniques to combine diverse sensor data efficiently while minimizing energy consumption.
The expected outcomes include a suite of distributed lightweight ML models suitable for deployment in resource-constrained environments, performance metrics, and best practice guidelines for CCAM and future networks.
The candidate will benefit from full funding for the 36-month duration, including a generous living allowance (€125,304.48), mobility allowance (€25,560), and family allowance (€17,820 if applicable). The research will be conducted in both Dublin, Ireland, and Thessaloniki, Greece, offering a unique opportunity for international collaboration and personal growth.
Candidate Profile
Ideal applicants for this PhD position will have:
– A strong academic background in Computer Science, Electrical Engineering, Data Science, Artificial Intelligence, or related fields.
– Experience or keen interest in machine learning, distributed systems, and IoT technologies.
– Proficiency in programming and familiarity with ML frameworks and tools.
– A research-oriented mindset, with problem-solving skills and the ability to work independently and collaboratively.
– Motivation to contribute to real-world applications in privacy-aware, real-time intelligent systems.
Applicants should also be enthusiastic about working in multicultural environments and willing to spend significant time in both Ireland and Greece.
Application Process
The application deadline for this position is April 25th, 2026.
To apply, candidates are required to fill out the online application form and attach a cover letter and CV. For full details and to access the application form, please visit the official vacancy page:
https://mas3.ucd.ie/vacancies/
Conclusion
This fully funded PhD position offers a unique opportunity to contribute to the advancement of edge-enabled distributed machine learning in an international, interdisciplinary setting. If you are driven to make a meaningful impact in the field of intelligent systems and enjoy working at the forefront of technology, you are encouraged to apply and take the next step in your academic career. Stay connected for more opportunities like this by following updates on leading research positions.
Questions & Answers
Question: What are the main benefits of pursuing a PhD at University College Dublin?
UCD offers a world-class research environment, strong industry collaborations, access to advanced facilities, and a multicultural academic community in one of Europe’s most dynamic cities.
Question: Will the PhD candidate spend time in both Ireland and Greece?
Yes, the program is designed for the candidate to spend equivalent time at University College Dublin in Ireland and at Pragma-IoT in Thessaloniki, Greece, providing a rich international experience.
Question: Is this position suitable for candidates from non-EU countries?
Yes, the position is open to international candidates. The funding package includes allowances for mobility and family, supporting researchers from diverse backgrounds.
Question: What kind of research outputs are expected from this project?
The project aims to deliver distributed lightweight ML models for heterogeneous edge devices, multi-modal data fusion techniques, and best practice guidelines for connected automated mobility and future networks.
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