Job Information
Organisation/Company: University College Dublin
Research Field: Computer science » Autonomic computing
Researcher Profile: Recognised Researcher (R2)
Application Deadline: 17 Aug 2026 - 10:56 (UTC)
Country: Ireland
Type of Contract: Temporary
Job Status: Full-time
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
Applications are invited for a temporary post of a UCD Post-doctoral Research Fellow Level 1 within UCD School of Computer Science.
HD maps (High Definition Maps) offer detailed, up-to-date information about the surrounding environment, enabling advanced safety and navigation capabilities, particularly for vehicles in autonomous or semi-autonomous driving modes. HD maps are crucial for highly automated driving (HAD), providing high-precision features like traffic signs and lane boundaries that demand submeter absolute accuracies. Typically, specialized mapping vehicles, equipped with advanced sensors such as 3D laser range finders, high accuracy GNSS receivers, and premium inertial sensors, are employed for HD map creation. However, due to their high cost, access to these vehicles is limited.
The main three tasks of this project focus on Cooperative Data Collection, Federated Data Analytics, and Dynamic Task Orchestration. These tasks entail the integration of specific AI algorithms to enable efficient and collaborative data gathering processes. In Cooperative Data Collection, techniques such as distributed task allocation algorithms, can be employed to allocate data collection tasks among vehicles or agents efficiently. Federated Data Analytics can leverage federated learning algorithms, including Federated Averaging and Federated Proximal methods, to collaboratively train machine learning models across distributed agents while preserving data privacy. Dynamic Task Orchestration involves the use of reinforcement learning algorithms, such as Deep Q-Networks (DQN) or Proximal Policy Optimization (PPO), to dynamically allocate tasks and optimize agent behaviours based on real-time environmental changes. In this project, the system will be built as Multi-agent Systems (MAS) to represent real-world characteristics, such as autonomy at the vehicle level and cooperation capabilities in Connected Autonomous Vehicles (CAVs). The vehicles or agents within the MAS will employ reasoning mechanisms, such as Bayesian inference or logical reasoning, to identify changes in the environment and coordinate their behaviour (e.g., speed, acceleration, and positioning) accordingly to capture the necessary data for updating the High Definition (HD) maps.
PD1 Salary Range: €47,273 - €52,513 Per Annum
Appointment on the above range will be dependent upon qualifications and experience.
Closing date: 12:00 noon (local Irish time) on 17 August 2026.
Applications must be submitted by the closing date and time specified. Any applications which are still in progress at the closing time of 12:00 noon (Local Irish Time) on the specified closing date will be cancelled automatically by the system. UCD are unable to accept late applications.
UCD do not require assistance from Recruitment Agencies. Any CV's submitted by Recruitment Agencies will be returned.
The PD1 position is intended for early-stage researchers, either just after completion of a PhD or for someone entering a new area for the first time. If you have already completed your PD1 stage in UCD or will soon complete a PD1, or you are an external applicant whose total Postdoctoral experience, inclusive of the duration of the advertised post, would exceed 4 years, you should not apply and should refer to PD2 posts instead.
Prior to application, further information (including application procedure) should be obtained from the Work at UCD website: https://www.ucd.ie/workatucd/jobs/
Where to apply
Website: https://www.aplitrak.com/?adid=eWFzZW1pbi5vemRlbWlyLjU2NzA2Ljk5MDhAdW5pY2R1Ymxpbi5hcGxpdHJhay5jb20
Requirements
Research Field: Computer science
Education Level: PhD or equivalent
Skills/Qualifications
Communication and interpersonal skills, Computer Science, Transport Engineering, Experience with Mobility Simulators, Expertise in computing paradigm, specifically edge computing, Autonomous mobility perception systems and HDmaps
Additional Information
Eligibility criteria
Mandatory:
- PhD in Computer Science, Transport Engineering, and other relevant fields.
- Extensive experience with Mobility Simulators.
- Expertise in computing paradigm, specifically edge computing.
- A demonstrated commitment to research and publications.
- An understanding of the operational requirements for a successful research project.
- Evidence of research activity (publications, conference presentations, awards) and future scholarly output (working papers, research proposals, and ability to outline a research project.
- Excellent Communication Skills (Oral, Written. Presentation etc).
- Excellent Organisational and Administrative skills including a proven ability to work to deadlines.
- Candidates must demonstrate an awareness of equality, diversity and inclusion agenda.
The PD1 position is intended for early-stage researchers, either just after completion of a PhD or for someone entering a new area for the first time. If you have already completed your PD1 stage in UCD or will soon complete a PD1, or you are an external applicant whose total Postdoctoral experience, inclusive of the duration of the advertised post, would exceed 4 years, you should not apply and should refer to PD2 posts instead.
Desirable:
- Experience in data modelling and data fusion.
- Experience in autonomous mobility perception systems and HDmaps.
- Experience in setting own research agenda.
Selection process
https://www.ucd.ie/workatucd/jobs/
Website for additional job details: https://www.ucd.ie/
Work Location(s)
Number of offers available: 1
Company/Institute: University College Dublin
Country: Ireland
City: Dublin
Contact
Website: https://www.ucd.ie/
E-Mail: hrhelpdesk@ucd.ie
Phone: 01 716 4900
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