Candidates interested in applying to the MSCA Postdoctoral Fellowship 2024 call with an SCSS faculty member have to complete the Expression of Interest form available at this link by 15th June 

The MSCA Postdoctoral Fellowships Programme funds researchers holding a PhD who wish to acquire new skills through advanced training, international, interdisciplinary and inter-sectoral mobility. The programme is open to researchers of any nationality and the funding includes competitive living and mobility allowance as defined by the European Commission along with a family, long-term leave and special-needs allowances, as applicable. 

TCD School of Computer Science and Statistics (SCSS) thrives on research excellence and innovation with a broad range of themes including Artificial Intelligence, Data and Cybersecurity, Human Computer Interaction, Augmented/Virtual Reality, Software Performance, Future Networks & Internet of Things, Digital Content Technology, Statistics and Data Science, and has a rich history of collaboration with industry, developing intellectual property and creating new ventures.

SCSS leads two large-scale national research centres focusing on future networks & communications (CONNECT Research Centre) and AI-driven digital content technology (ADAPT Research Centre). The School strives to provide a friendly, collegiate environment built upon the foundation of mutual respect and equality of opportunity which allows, encourages and facilitates individuals, teams and groups to excel in research, teaching, innovation and outreach.  

The Expression of Interest forms will be initially reviewed with regard to eligibility criteria* and research interest affinity with the faculty member(s) chosen in the application form by the applicants. If deemed eligible, the candidates will be introduced to the faculty member to discuss a potential proposal for submission to the MSCA Postdoctoral Fellowships call 2024. The proposal development process will also be supported on a one-to-one basis by the Research Funding Specialists in the School`s Research Unit. Additional proposal training is available from the Irish Universities Association MSCA Office. Deadline for submission of proposals is 11th September. 

 For any queries about the Expression of Interest form, eligibility and MSCA Postdoctoral Fellowship 2024 call, pleases contact the SCSS Research Unit ( 


* Eligibility Criteria for Applicants to the MSCA Postdoctoral Fellowship Call 2024:  

  • Candidates must be in possession of a PhD degree and have up to 8 years’ experience in research from the date of the award of their PhD degree to apply for 2-year European Postdoctoral Fellowships (PF), a Horizon Europe Marie Skłodowska-Curie Action. 
  • Candidates must not have carried out their main activity (work or study) or lived in the Republic of Ireland for 12 months in the 3 years prior to call deadline. 

Our academics offering supervision and their research interests for developing a proposal to the MSCA PF programme:

Alessio Benavoli Developing machine learning models that learn from and adapt to human preferences and choices, with a particular focus on incorporating Bayesian optimisation and reinforcement learning techniques. Learning from human choices is crucial as it enables the creation of more intuitive, user-centric systems that can better predict and meet the needs of individuals, leading to improved user satisfaction and more effective decision-making.

Anthony Ventresque Mutation Analysis, Testing the quality of A-based systems (chatbots, computer vision, machine learning), Data cente optimisation. Improving the quality of modern software systems with a view to apply to a number of practical real-world software engineering areas including Conversational Agents, Assistive Technologies and Cloud/data centres optimisation

Binh-Son Hua 3D content creation, computer graphics, computer vision, and machine learning with a focus on generative AI in 3D visual computing. Transforming the accessibility of 3D assets by delving into the synergy of multi-modal data, including text, images, and videos.

Gareth Young Enhancing user experiences by creating more immersive and intuitive ways of interacting with digital systems. Exploring immersive HCI, natural interaction methods, universal accessevility, creative innovation in the arts through VR

Ivana Dusparic Developing robust multi-agent RL systems for optimization of resource use in large-scale infrastructures (e.g., communication networks, transportation networks). Explainability of multi-agent RL systems, Transfer learning in multi-agent RL,Software testing of RL-based systems

Marco Ruffini Developing machine learning systems that can recognise the type of disturbance in an optical fibre communications, reading real data from fibre sensing probes. Developing AI-driven control plane mechanisms that can maximise the accuracy and usability of optical sensing probes across metropolitan networks.

Mimi Zhang Methodological or application oriented projects related to cluster analysis to advance statistical methods for processing data in real-life scenarios

Rachel Mc Donell Multimodal machine Learning for computer animation, such as generation of co-speech gestures for virtual embodied agents, Perception of embodiment in Virtual Reality, Perception of Virtual Humans

Subrahmanyam Murala Harnessing deep learning and multimodal learning to advance computer vision techniques and improve their precision. Developing Deep Learning models in areas such as image/video restoration, video motion magnification, moving object segmentation, deepfake generation and detection, 2D/3D object inpainting/outpainting, burst superresolution, medical signal/image analysis, depth estimation, affective computing.

Viet Quoc Pham Developing new AI/ML techniques with a particular focus on domain and knowledge generalisation in federated learning. Designing new model-driven and AI-based algorithms for resource management in large-scale 6G networks.

Vinny Cahill Designing new ML-based techniques for cyber-physical systems having particular focus on sustainable mobility with the goal of optimizing travel-time reliability for travellers and freight. Proposals addressing the development of novel multi-agent reinforcement learning and warm intelligence algorithms for large-scale system optimisation are especially welcome