Theses
We have thesis proposals available for a wide range of topics in AI, HPC, and Large-Scale Deep Learning. All thesis activities can continue with a PhD or research contract positions, or with a position in a company.
All theses are conducted within an experienced team, which can train and consistently support you - also in collaboration with companies within the industry. For all theses, the publication of at least one scientific paper is possible (and encouraged), as is the continuation of your academic path through a PhD or an internal research period. For those interested in the corporate world, we maintain strong connections with companies involved in these areas, which may consider employment opportunities.
Available thesis topics
Trustworthy and Safe AI
Large-Scale architectures are trained on billion of web-scraped uncleaned items, and this might lead to unsafe or toxic behaviours. We aim at designing approaches for making Large-Scale models safe and trustworthy. This can be applied to both embedding models (e.g. CLIP) or LLMs. The activity can also include the design of novel DeepFake detection algorithms, and is in done as part of the ELSA and ELIAS EU projects.
🟩 Can lead to research publications
Publication date: 04/06/2025
Design of SVG generation algorithms
Transformer-based architectures for generating images in SVG format, thus with infinite resolution, starting from textual prompts. Models can be designed on the basis of LLM architectures, and they might employ diffusion-based strategies.
🟩 Can lead to research publications
Publication date: 04/05/2025
Connecting AI and HPC
As part of the MINERVA EU project, we wish building approaches to ensure that European AI communities can harness the power of the EuroHPC systems. The thesis will investigate large-scale architectures, community needs across Europe, develop training courses and best practices for leveraging HPC systems.
🟩 Can lead to research publications
Publication date: 04/05/2025
Novel Retrieval-Augmented Generation Techniques
Retrieval-Augmented Generation (RAG) enhances the reasoning and factual consistency of generative models by grounding their responses in external knowledge retrieved at inference time. In this thesis, we aim to explore novel RAG techniques that improve end-to-end performance, coherence, and scalability. The thesis may involve designing new architectures, proposing improved training strategies, or benchmarking on open-domain QA, code generation, or document summarization. The activity is aligned with ongoing research in the context of foundation models and may contribute to publications and open-source tools.
🟩 Can lead to research publications
Publication date: 04/20/2025
Multimodal LLMs for Structured Document Translation
This thesis explores the development of Multimodal Large Language Models capable of translating between rich-format PDF documents and ITX, a structured template format for generating PDF files. The activity involves training and evaluating vision-language models that can parse document layouts, understand visual and textual content jointly, and generate editable ITX representations. Applications span document automation, archiving, and accessibility. The work will focus on model design, fine-tuning strategies, and alignment between visual structure and template semantics.
🟩 Can lead to research publications
Publication date: 04/20/2025
Multimodal Large Concept Models
Large Concept Models (LCM) are a compelling alternative to traditional token-based LLMs, where tokens are replaced by dense vectors (representing higher level "concepts") as provided by a textual embedding space. This paradigm shift has, so far, being investigated for the case of text. In this thesis we aim at explore the frontier of building a Multimodal version of an LCM and extending the idea of conceptualization to both text and images.
A relevant impact is expected in terms of scientifc publications.
🟩 Can lead to research publications
Publication date: 02/24/2025
Learning to Schedule: AI-Driven Optimization of SLURM Queues in HPC Systems
Efficient job scheduling is critical to maximizing the utilization and throughput of HPC systems. Traditional queueing policies in SLURM are often static and suboptimal under varying workloads. This thesis explores the development of learnable algorithms - such as reinforcement learning or graph-based predictors - for dynamically optimizing job scheduling within SLURM environments. The work involves collecting real-world queue traces, modeling job behaviors, and designing intelligent schedulers capable of adapting to system load, job priority, and resource availability.
🟩 Can lead to research publications
Publication date: 04/21/2025
Design of Super resolution architectures
Design of innovative architecture for increasing the resolution of images. Models can be based on Attentive and Convolutional-based modeling, and will be tested on indoor scenes as well as natural images.
🟩 Can lead to research publications
🕐 Only one position remaining
Publication date: 04/05/2025
Large-Scale Multimodal Models
Large Multimodal Models integrate Vision and Language towards the achievement of human-like conversation abilities. The theses in this context explore novel architectures, multimodal connectors, novel training strategies and evaluation scenarios.
🟩 Can lead to research publications
Publication date: 02/24/2025
Deepfake detection algorithms
The proliferation of AI-generated Deepfakes poses serious threats to trust and security. This thesis will focus on the design and development of robust Deepfake detection algorithms, exploring both supervised and self-supervised approaches. Techniques can leverage multi-modal cues, visual artifacts, or anomalies in embedding spaces. The thesis can include benchmarking against existing datasets and participation in international challenges. This research is aligned with ongoing activities within the ELSA EU project.
🟩 Can lead to research publications
Publication date: 04/20/2025
Agentic AI
Agentic AI refers to generative systems capable of autonomous goal-setting and reasoning, interacting with external tools and the environment. We aim to explore innovative architectures and training strategies for building novel agentic systems that can perform complex tasks. This includes the design of modular memory systems, multi-step reasoning chains, and self-reflective planning mechanisms. Applications span virtual assistants, robotic control, and automated research agents.
🟩 Can lead to research publications
Publication date: 04/20/2025