Vacancies

Post Summary
Novel optical access and metro networks are essential to support fast growing capacity requirement, driven by future applications and services for 6G and beyond. Networks are also 
evolving to support a large variety of signals across larger access and metro network areas. For these novel scenarios, flexibility becomes an essential feature, driven by the development of flexible components and open networks. 

Development of Digital Twins, capable of building dynamic models from information gathered form live networks become essential to manage network requests, while maintaining high availability and optimal resource utilization.

Two Post Doctoral researcher position are available for work in the area of Digital Twins for optical networks, making use of intelligent AI-driven control planes. The work will involve: i) (i) development of network probing algorithms for OSNR/GSNR measurement automation; (ii) development of adaptive ML and critical component analysis, and (iii) development of 
Automation of ML model and data selection.

The applicants should have a solid theoretical background on machine learning and optical networks and willing to engage in testbed-oriented research. The work will be carried out in the new OpenIreland research infrastructure, a large-scale testbed for experimentation in open optical, wireless and cloud technologies.

The position will be based in the CONNECT research centre at Trinity College Dublin, Ireland and will be funded under the Science Foundation Ireland Investigators award project. The 
researcher will have the opportunity to build their research portfolio within a range of projects within CONNECT. The researcher will also be part of the School of Computer Science and Statistics in TCD. 

The position will be under the direction of Prof. Marco Ruffini and Prof. Dan Kilper. 

Full Description: PD Twilight

We are seeking a highly motivated candidate for a fully funded postdoctoral researcher position to work in 3D computer graphics and 3D computer vision.

The successful candidate will join the 3D Graphics and Vision research group led by Prof. Binh-Son Hua at the School of Computer Science and Statistics, Trinity College Dublin, Ireland to work on topics related to generative AI in the 3D domain. 

The School of Computer Science and Statistics at Trinity College Dublin is a collegiate, friendly, and research-intensive centre for academic study and research excellence. The School has been ranked #1 in Ireland, top 25 in Europe, and top 100 Worldwide (QS Subject Rankings 2018, 2019, 2020, 2021).

The postdoctoral researcher is expected to conduct fundamental research and publish in top-tier computer vision and computer graphics conferences (CVPR, ECCV, ICCV, SIGGRAPH) and journals (TPAMI, IJCV). Other responsibilities include supporting graduate or undergraduate students with technical guidance and engagement in other research activities such as paper reviews, reading group, workshop organization, etc. 

The start date of the position is August 01, 2024. Contract duration is 1 year with the 
option of renewing for a second year. 

Full Description: Postdoctoral Researcher 3D Graphics & Vision

Post Summary
Novel optical access and metro networks are essential to support fast growing capacity requirement, driven by future applications and services for 6G and beyond. Networks are also 
evolving to support a large variety of signals across larger access and metro network areas. For these novel scenarios, flexibility becomes an essential feature, driven by the development of flexible components and open networks.

Development of Digital Twins, capable of building dynamic models from information gathered form live networks become essential to manage network requests, while maintaining high availability and optimal resource utilization.

Three PhD studentships are available for work in the area of Digital Twins for optical networks, making use of intelligent AI-driven control planes. The work will involve: i) (i) development of network probing algorithms for OSNR/GSNR measurement automation; (ii) development of adaptive ML and critical component analysis, and (iii) development of Automation of ML model and data selection.

The applicants should have knowledge of machine learning and optical networks and willing to engage in testbed-oriented research. The work will be carried out in the new OpenIreland research infrastructure, a large-scale testbed for experimentation in open optical, wireless and cloud technologies.

The position will be based in the CONNECT research centre at Trinity College Dublin, Ireland and will be funded under the Science Foundation Ireland Investigators award project. The 
researcher will have the opportunity to build their research portfolio within a range of projects within CONNECT. The researcher will also be part of the School of Computer Science and Statistics in TCD. 
The position will be under the direction of Prof. Marco Ruffini and Prof. Dan Kilper.

Full Description:  PhD Twilight

We are seeking a highly motivated candidate for a fully funded PhD position to work in 3D computer graphics and 3D computer vision. 

The successful candidate will join the 3D Graphics and Vision research group led by Prof. Binh-Son Hua at the School of Computer Science and Statistics, Trinity College Dublin, Ireland to work on topics related to generative AI in the 3D domain. 

The School of Computer Science and Statistics at Trinity College Dublin is a collegiate, friendly, and research-intensive centre for academic study and research excellence. The School has been ranked #1 in Ireland, top 25 in Europe, and top 100 Worldwide (QS Subject Rankings 2018, 2019, 2020, 2021).

The PhD student is expected to conduct fundamental research and publish in top-tier computer vision and computer graphics conferences (CVPR, ECCV, ICCV, SIGGRAPH) and journals (TPAMI, IJCV).

The start date of the position is September 01, 2024. The position is fully funded for 4 years by Science Foundation Ireland.

Full Details: PhD 3D Graphics & Vision

Post Summary

PhD Project: Understanding the Modelling the Dynamics of Stroke Risk Across the Life-course of Women

Being able to predict stroke risk (both for a first ever stroke and a recurrent stroke), and better understanding of the factors that determine stroke risk, could enable better targeting of prevention strategies. Stroke risk factors can broadly be divided into ‘fixed’ and ‘modifiable’. Fixed factors include age and sex, whilst modifiable/treatable risk factors include smoking, hypertension, diabetes, physical activity and diet.

The profile of stroke risk is different in men and women because of several different factors including hormonal and pregnancy-related factors. Yet in clinical practice, primary and secondary stroke prevention (and also prevention for cardiovascular disease) is addressed in similar ways for men and women. The focus of this project is on analysing the effect of women-specific life events (e.g., oral contraception, pregnancy, HRT, menopause, etc.) on stroke risk. The overall vision is to create a stroke risk prediction tool for women (similar to those that exist for women with breast cancer1) to predict the risk of first-ever and recurrent stroke; this could allow women to make better-informed choices about medical treatment and lifestyle. The research will adopt a data-driven approach, involving data science/statistics/machine learning methods, and will be informed by clinical literature and practice.

Standard Duties and Responsibilities of the Post

The successful candidate will be registered to the structured PhD programme in the School of Computer Science and Statistics. They will be required to work full time on their PhD which includes mandatory demonstrating duties and optional teaching activities. The appointed applicants will undertake academic research under the direction of the PI. The PhD candidate’s specific duties will include:

· Undertake research leading to a PhD;

· Produce academic papers and reports throughout the course of their PhD;

· Meet with supervisors regularly, attend and contribute to research group meetings, journal clubs, and communicate research findings at national and international conferences.

· Collaborate with colleagues in the School and the ADAPT research centre;

Closing Date: 1st July, 2024

Full Description: PhD Women in Stroke

About the Project

Offering predictable journey times is important to the uptake of sustainable road transportation including future public, shared, and on-demand mobility services and to ontime delivery of goods. To achieve such predictability, the ClearWay1 project at TCD is exploring ‘slot-based driving’ (SBD) as a strategy for active management of roads (especially
highways). SBD abstracts traffic as flows of slots with each slot having a specified trajectory. Vehicles are associated to slots and their trajectories are intended to follow those of their 
assigned slots. In practice, depending on the level of control available and/or the degree of compliance of drivers with guidance, vehicles may depart from their slots. However, slots 
provide an abstraction over which the road traffic management system can reason and exercise control. In particular, we are exploring the use of reinforcement learning to have 
controllers learn appropriate slot-management strategies.

This project will develop a simulation environment to allow the evaluation of the potential benefit of SBD in realistic traffic scenarios initially focusing on highways but extending to 
urban environments if time permits. 

Skills Required

Excellent computer programming skills (preferably using Python) as well as: 
For post (1) ideally previous experience of computer simulation preferably using SUMO;
For post (2) mandatory previous experience of machine learning, preferably reinforcement learning, and ideally experience with mobile robotics and/or embedded systems;
For post (3) mandatory previous experience of developing distributed applications, preferably with AWS

The positions are tenable immediately for up to twelve weeks (expected to finish no later than the 30th of August 2024) and interns will be expected to work primarily on campus. 

Full Details: Clearway Internship