Vacancies

The Purpose of the Role
The School of Computer Science and Statistics is seeking to appoint an IT Systems Administrator 
on a 50% FTE specific purpose contract to cover the career break of a permanent Systems 
Administrator.
Plan, organise, deploy and manage state-of-the-art systems and technologies within the School of 
Computer Science and Statistics (SCSS). The Systems Administrator plays an integral and crucial role 
within the School of Computer Science and Statistics, ensuring the smooth functioning, security, 
and efficiency of both Windows and Apple Mac environments. This pivotal position involves 
meticulous management, maintenance, and optimization of computer labs, emphasizing precise 
imaging and configuration of lab machines to meet rigorous standards.
The successful candidate will be responsible for providing high-level contributions and leadership, 
devising innovative solutions to complex problems encountered by students, teaching staff, 
research groups and administrators within the School.

Closing Date: 12:00 noon on 30^th July 2024

All applications should be made via the Trinity Jobs Portal http://jobs.tcd.ie 

Full Job Description

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
This post will be under the SFI Frontiers for the Future RADICal project (RADICal: on-set virtual Reality for Actors portraying DIgital Characters). The main aim of the RADICal project is to transform the traditional approaches for face and body capture of film actors using real-time motion capture and Virtual Reality (VR) to allow them to visualise their virtual faces and bodies from a first-person perspective, live on-set. We hypothesize that this will greatly enhance their performances and overall satisfaction in playing the role of a virtual character. The main novelty of our approach is incorporating actor methods and practices into the 
technical algorithms for motion tracking and mapping and conducting neuroscience experiments to enhance the illusory perception that a virtual body or body-part is the actors own.

This particular role will be related to using machine learning to enhance embodiment of humans and animals, and conducting perceptual experiments on embodiment in VR. This will involve investigating new machine learning algorithms or physics simulation to allow for more accurate embodiment of both humans and animals. Additionally, perceptual experiments will be conducted both on the actors immersed in VR and also on the viewers of the recorded performances of the actors (i.e., the movie audience), and the results will be fed-back to the system to inform the technical decisions

Full Job Description

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

We are seeking a highly motivated candidate for a fully funded PhD position to work in Multimodal Learning for Computer Vision applications.

The successful candidate will join the Computer Vision and Pattern Recognition Lab, led by Prof. Subrahmanyam Murala, at the School of Computer Science and Statistics, Trinity College Dublin, Ireland. The research will focus on developing deep learning-based solutions for various computer vision applications, providing an excellent opportunity to work at the forefront of this rapidly evolving field. You can find more information about Prof. Subrahmanyam Murala and his work at https://www.scss.tcd.ie/~muralas/.

The School of Computer Science and Statistics at Trinity College Dublin is a collegial and research-intensive center known for academic excellence. It has been consistently ranked as the #1 computer science school in Ireland, among the top 25 in Europe, and within the top 100 worldwide according to the QS Subject Rankings in 2018, 2019, 2020, 2021 and 2023.

The PhD student is expected to conduct fundamental research in the field of computer vision, aiming to advance the current state of knowledge. The student will be encouraged to publish their findings in top-tier conferences such as CVPR, ECCV and ICCV, as well as in leading journals including IEEE Transactions on Image Processing (TIP), IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), and the International Journal of Computer Vision (IJCV). This will provide the student with the opportunity to contribute to and be recognized by the global research community.

The start date of the position is September 01, 2024. The position is fully funded for 4 years by the School of Computer Science and Statistics, Trinity College Dublin, Ireland.

The successful candidate will require the following skills and knowledge:

● Bachelor or Master in Computer Science or Electrical Engg. or Electronics and Communications Engg. or related fields.

● Strong competence in computer vision, signal and image processing, deep learning.

● Solid experience in academic research and publications is an advantage.

● Additional background in math, statistics, or physics is an advantage.

● Hands-on experience in training deep learning models.

● Hands-on experience in computer vision application development such as OpenCV, TensorFlow, PyTorch, CUDA

● Strong programming skills in C++, Python. Capability in implementing systems based on open-source software.

Applicants should provide the following information:

● A comprehensive CV.

● Academic transcripts of Bachelor and Master’s degree.

● The name and contact details of two referees.

Interested candidates are encouraged to contact Prof. Subrahmanyam Murala (https://www.scss.tcd.ie/~muralas/ ) via email for an informal discussion about the PhD position. The deadline for application submissions is July 15, 2024. Prospective candidates are encouraged to submit their applications as early as possible to ensure they are considered for this exciting opportunity.

Full Description

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