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Research Fellow (Postdoctoral) - Two positions

Trinity College Dublin, The University of Dublin - CONNECT Research Centre - Discipline of Networks and Distributed Systems - School of Computer Science and Statistics

Location: Dublin - Ireland
Salary: €44,847 to €55,740 or £39,007.74 to £48,482.43 (converted salary*)
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 28th November 2024
Closes: 20th December 2024
 

Post status: Specific Purpose Contract – Full-time

Location:

Trinity College Dublin, The University of Dublin,
College Green, Dublin 2, Ireland

Reports to: Dr. Ivana Dusparic

Salary:

Commensurate with experience and achievement on the SFI Team Member Budget Scale (October 2024) Post Doctoral Researcher Level 2A/B (in the range €44,847-€55,740)

Hours of work: 39 hours per week

Closing date: 17:00 (BST) on Friday 20th of December 2024. Late applications will be considered if the post remains unfilled.

Post Summary

We are seeking to recruit two post-doctoral research fellows to investigate novel reinforcement learning (RL) algorithms and their applications in IoT and edge cloud networks, within CONNECT - the Science Foundation Ireland Research Centre for Future Networks and Communications.

Position 1 (IoT networks): This project will investigate the use of multi-agent RL for sensor-scheduling in energy-constrained environments. Developed algorithms will need to be lightweight and are required to run on energy and resource-constrained devices. Algorithms will utilize Age of Information metric to learn appropriate update interval, taking advantage of observed correlation to reduce sensor system energy usage.

Position 2 (edge cloud networks): This project will investigate the use of multi-agent and multi-objective RL for optimisation of resource allocation in edge cloud networks.

The successful candidates are expected to make contributions to the state of the art in RL algorithms and their applications in communication networks.

The positions will be based in the CONNECT Research Centre at Trinity College Dublin under the direction of Dr. Ivana Dusparic, working in close collaboration with colleagues at other CONNECT partner Universities. In particular, position 1 will be involved close collaboration with Tyndall Institute, utilizing their renewable energy testbed.  

For informal inquiries please contact ivana.dusparic@tcd.ie.

Person Specification

  • A PhD in Computer Science, Computer Engineering, Electronic Engineering, or a closely related field.
  • An established track record of publication in leading journals/conferences on topics relevant to project.
  • Demonstrates expertise in reinforcement learning and its applications in large-scale applications.
  • Demonstrates excellent software engineering and programming skills.
  • Demonstrates the ability to generate new ideas and link and build upon existing ideas to generate unique concepts and solutions with integrity.
  • Demonstrates knowledge and understanding of the policy, practices and procedures that are relevant to the role including knowledge and understanding of research ethics, the broader University sector and external sponsor or funder policies.

Application Procedure

Please send applications by email to Ivana.Dusparic@tcd.ie with “RL CONNECT Fellowship” in the subject line, containing three PDF files as follows:

  1. A cover letter
  2. A curriculum vitae (giving full details of qualifications and experience, including transcripts of degrees, a description of your contribution to relevant project work, identification of your three most-significant publications relevant to the ClearWay project, and the names and contact details of two referees)
  3. A 2 page research proposal outlining how you might address the research goals of the projects listed and how does this advance the state of the art

Please do not provide other documents, documents in other formats, or include any substantive information in the body of your email.

Closing date: 17:00 (BST) on Friday 20th of December 2024. Late applications will be considered if the post remains unfilled.

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