The PhD programme in High Performance Scientific Computing (HPSC) is an interdepartmental and industry-driven initiative, established through a collaboration between several departments of the University of Pisa, the company S.I.T. – Sordina IORT Technologies, and main national research institutions.
The participating departments are:
- Department of Mathematics
- Department of Computer Science
- Department of Information Engineering
- Department of Civil and Industrial Engineering
- Department of Physics
- Department of Geosciences
- Department of Chemistry and Industrial Chemistry
- Department of Pharmacy
In collaboration with the following research centers:
- National Institute of Geophysics and Volcanology
- National Institute for Nuclear Physics
- Institute of Information Science and Technologies of the National Research Council of Italy
- Institute for Applied Computing “Mauro Picone” of the National Research Council of Italy
The programme is aimed at young researchers who wish to develop advanced skills in high performance scientific computing, addressing complex problems from an interdisciplinary perspective and with strong ties to the industrial world.
The programme trains a new generation of scientists capable of designing innovative solutions using cutting-edge tools in High Performance Computing (HPC), Artificial Intelligence, and numerical simulation. Applications range from mathematics to physics, from engineering to life sciences, from chemistry to climate science, and even to health and medicine.
Educational Objectives
The central aim of the HPSC PhD programme is to provide highly specialised theoretical and practical training in high-performance computing technologies. The programme supports doctoral candidates on a comprehensive learning journey that includes:
- The design and optimisation of scalable algorithms;
- The development of advanced software for supercomputers and parallel architectures;
- The application of Machine Learning and Artificial Intelligence techniques to the analysis of large-scale data;
- The integration of computational tools in strategic scientific and technological contexts, such as climate modelling, biomedical research, computational engineering, and materials science.
Through personalised teaching and a modular training structure, the programme enables PhD candidates to address any disciplinary gaps and to build a strong, forward-thinking professional profile.
Interdisciplinary Research and Industrial Collaboration
A defining feature of the HPSC Ph.D. programme is its strong integration between academic research and the industrial sector. Each doctoral project is designed to foster collaboration across multiple departments and to directly involve industrial partners and research centres, thereby promoting technology transfer and the adoption of HPC solutions in real-world applications.
PhD candidates will have the opportunity to undertake research placements at the premises of the participating companies and research centres, engaging in real projects and contributing to the development of new technologies. For projects in collaboration with S.I.T., in particular, candidates will have access to radiofrequency, vacuum, and ionising radiation dosimetry laboratories, as well as participate in experimental activities related to Flash radiotherapy, one of the company’s key areas of focus.
These experiences will allow Ph.D. students not only to deepen their understanding of cutting-edge scientific and technological challenges, but also to gain insight into industrial processes and the requirements for translating research into practical solutions.
Research Facilities and Infrastructure
GrThe programme benefits from a broad and well-structured network of facilities, laboratories, and computational infrastructure. Key resources include:
- Department of Mathematics: computing laboratories (Aula DM 3-Lab and Aula DM 4-Lab);
- Department of Information Engineering: FoReLab and CrossLab laboratories, dedicated to advanced technologies and intelligent systems;
- Green Data Center of the University of Pisa: access to HPC clusters for large-scale numerical simulations and computations, including collaborative work with specialised research groups;
- Department of Pharmacy: laboratories for validating models and algorithms using real biological data, including facilities for cell manipulation, molecular analysis, spectroscopy, and gene editing.
In addition to these resources, Ph.D. candidates will have access to study spaces, meeting rooms, and collaborative environments within the participating departments. They will also have the opportunity to attend seminars, workshops, and roundtables with researchers and professionals from around the world.
Main Research Topics
The programme offers students advanced knowledge and research opportunities across several specialised areas, including:
Computational Mathematics and Algorithm Development
Focus on the design of advanced mathematical models and optimised algorithms for HPC environments. Topics include numerical methods for parallel computing, optimisation techniques, and simulation methods applicable to various scientific domains. Research leverages the expertise of the Mathematics and Computer Science departments in areas such as numerical linear algebra, finite element methods, reduced models, and optimisation.
HPC Software and Systems
Development of scalable, efficient, and robust HPC software systems. This research area covers improvements in supercomputing architecture, parallel computing, and operating systems, aiming to enhance computational efficiency and energy sustainability. It draws on expertise from the Computer Science and Information Engineering departments.
Data Science and Big Data Analytics
Design of new HPC methods and solutions for managing, processing, and analysing large datasets in scientific research. Topics include parallel algorithms for machine learning, statistical methods, and data visualisation for extracting insights from complex data across multiple domains.
Computational Engineering
Application of HPC to complex engineering problems, including but not limited to fluid dynamics, energy network and device optimisation, materials science, computational electronics, intelligent systems design, structural analysis, biomedical engineering, computational neuroscience, neuromorphic systems, and financial engineering. This area supports the development of innovative simulation methodologies with potential impacts in biomedical technologies, aerospace, civil and energy engineering, and more.
Physics and Quantum Computing
Use of HPC and distributed computing systems to analyse massive datasets (exa-bytes) in experimental and observational physics. Research includes development of compute accelerators in heterogeneous systems (GPU and FPGA), real-time applications, AI/ML techniques for simulation and data analysis, HPC infrastructures (CPU/GPU) for simulations (e.g. Lattice QCD, fluid and plasma dynamics), as well as quantum computing algorithms and the HPC-QC interface.
Chemistry and Molecular Modelling
Research into molecular modelling using classical, quantum, or multiscale descriptions and its HPC applications. Emphasis is placed on using machine learning to enhance classical and quantum methods for simulating molecular properties and processes in complex systems, with the aim of advancing chemical research and materials science.
Earth Sciences
Topics include simulation, processing, and probabilistic inversion of 2D, 3D, and 4D geophysical data, also using AI techniques; HPC for satellite SAR interferometry; parallel algorithms for SAR data processing and real-world physics modelling; numerical simulations for climate change research based on observational and proxy data; mathematical modelling of aquifer systems for water sustainability; simulation of magma/fluid propagation for volcanic risk mitigation; and geodynamic simulations to study long-term lithosphere evolution.
Life and Health Sciences
Use of HPC methods for managing, processing, and analysing biological and pharmacological datasets. Topics include molecular kinetics simulations of protein-protein interactions, modelling tools for cell functioning, signalling, and gene enrichment, identification of disease biomarkers via predictive tools, and support for drug design and discovery through predictive molecular models. In silico tools are also employed for clinical trial design and outcome evaluation.
Medical Physics
Development of advanced computational tools to enhance diagnostic and therapeutic technologies. Topics include radiotherapy (especially Flash radiotherapy), multiscale radiobiological modelling, medical imaging, and radiation dosimetry. Research activities involve Monte Carlo simulations of beam transport, modelling biological responses, and the application of AI to predict therapeutic effects and optimise treatment protocols. The integration of numerical methods and machine learning improves therapy planning and understanding of radiotherapy effectiveness..
The main industrial partner, S.I.T., has identified the following complementary research topics:
Advanced Computational Methods for Flash Radiotherapy and Multiscale Radiobiological Modelling
The goal is to develop innovative computational tools to optimise Flash radiotherapy by integrating physical simulations, radiobiological modelling, and artificial intelligence.
Key activities include:
- Beam Modelling and Simulation: Development of numerical models and Monte Carlo simulations to characterise temporal structures and optimise dose delivery;
- Computational Radiobiology: Implementation of multiscale models to analyse cellular and tissue response to Flash irradiation, leveraging machine learning and biomolecular simulations;
- AI for Optimisation: Application of deep learning to improve treatment planning, predict biological responses, and automatically optimise therapeutic parameters;
- Computational Medical Imaging: Development of advanced image processing techniques, including image enrichment;
- Big Data Analytics: Design of novel HPC methods and solutions to manage, process, and analyse large datasets from radiobiological experiments, with a focus on automated, operator-independent pattern and cluster identification;
- Measurement Instrumentation Optimisation: Development of advanced simulation techniques for optimising materials and design parameters of dosimetric instrumentation.
Career Opportunities
The Ph.D. in High Performance Scientific Computing (HPSC) prepares highly qualified professionals capable of working in both academic and industrial settings, thanks to their cross-disciplinary expertise and hands-on experience in real-world applications.
Key career paths include:
- Academic research and scientific work in universities and public or private research centres;
- Development and innovation roles within high-tech companies, startups, and industrial laboratories;
- Positions in HPC, Data Science, and Artificial Intelligence, with a focus on simulation, predictive analysis, and big data management;
- Collaborations with public bodies, government agencies, and international organisations in sectors such as healthcare, climate, energy, and aerospace.
The multidisciplinary profile and practical experience gained through the programme equip Ph.D. candidates to lead innovation in fields that are vital to the future of research and technology.
The HPSC Ph.D. programme is supported by European projects that promote excellence in computational science and innovation, including dealii-X and EoCoE.
