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HPC Engineering

What will I study?

This Study Programme is designed to equip you with the skills needed to tackle complex computational problems by combining knowledge from mathematics, computer science, and engineering. The curriculum places a strong emphasis on technological and scientific innovation, scalable solutions, and the multidisciplinary nature of real-world applications

The programme prepares you to design parallel applications based on mathematical and statistical models, to analyse large-scale complex data using the power of supercomputing, and to develop advanced solutions in the emerging field of quantum computing.

Throughout your studies, you will develop a broad set of integrated skills across key areas, enabling you to effectively address the challenges of High-Performance Computing.

A solid foundation in numerical methods for advanced simulations, with a focus on differential and discrete modelling, computational linear algebra, and numerical techniques for the discretisation and solution of partial differential equations, essential tools for describing and simulating complex physical phenomena.

Advanced skills in parallel and distributed programming, with in-depth knowledge of computational models, tools, and libraries for multicore CPUs, GPUs, and supercomputers in order to effectively leverage parallelism, reduce execution times, and tackle computationally intensive problems efficiently.

In-depth knowledge of advanced HPC architectures, ranging from vector and pipelined processors to heterogeneous systems with tensor accelerators and the latest quantum processors, enabling you to understand and exploit hardware features in the design of HPC software.

Advanced competencies in quantum computing developed through classes and projects focused on the design of algorithms and simulations on quantum systems. This specialisation area offers a comprehensive overview of quantum technologies that are reshaping the boundaries of computation.

Tools and techniques in deep learning and artificial intelligence for handling complex and unstructured data, supporting statistical analysis, predictive modelling, and the extraction of meaningful insights from large datasets. Recent advances in AI and its increasingly complex models rely heavily on the computational power of HPC systems

Direct exposure to multidisciplinary application domains, thanks to elective courses focused on various scientific and engineering fields (genomics, aerospace, energy, mechanics, finance…). In these areas, HPC enables the acceleration of complex data processing, enhancing simulation and numerical modelling in order to tackle real-scale problems

How is the study path structured?

The structure of the programme ensures a balanced integration of theory, hands-on practice, and project work, with a strong focus on developing skills that are immediately applicable in the job market or in advanced research settings.

The two-year and 120 ECTS structure allows for a coherent and gradual progression throughout the programme.

  • The first year provides a comprehensive and integrated overview of the core tools and dimensions of HPC. You will build a solid foundation in key areas — from numerical methods and parallel programming to advanced computing architectures and infrastructures, including quantum computing.
  • The second year focuses on thematic specialisation, direct engagement with HPC application domains, and the development of project-based autonomy. Thanks to a wide range of elective courses across various scientific and engineering fields, you can shape your study path according to your interests, career goals, or research ambitions.
Study Plan

Elective courses and topics

The highly multidisciplinary course offering allows you to personalise your learning path — from deepening methodological knowledge to exploring real-world application domains. Depending on the area you choose to focus on, you will have the opportunity to develop complementary skills that define different possible thematic specialisations within the programme, such as engineering applications, mathematical modelling, data-intensive computing, or quantum technologies for quantum computing.

Within a total of 120 ECTS credits, the Study Plan includes 40 ECTS of elective courses and 20 ECTS dedicated to the thesis project.

The recommended study plan distributes the 40 ECTS of elective courses according to the following structure:

10 ECTS of elective courses from related subjects (mathematics/statistics, electronics, telecommunications, quantum technologies), which complement your core competencies and support your specialisation interests. These credits can be allocated to courses in the following disciplinary areas:

  • Mathematical and Statistical Sciences: to strengthen your background in numerical, probabilistic, and statistical methods, as well as physical modelling — essential for addressing complex computational problems in HPC environments. These skills provide the foundation for developing scalable, robust, and efficient algorithmic solutions.
  • Electronics and Telecommunications Engineering: to gain advanced knowledge in the design, implementation, and testing of devices, circuits, and systems for signal processing and transmission. These technologies form the infrastructure underpinning many computing architectures and advanced communication networks, which are key components in HPC systems.
  • Quantum-related disciplines: to explore the fundamentals of quantum technologies, a rapidly evolving and emerging field that is reshaping the computing paradigm by offering novel solutions to problems intractable for classical systems. These courses provide both a theoretical and practical basis for students interested in developing quantum algorithms and architectures.

10 ECTS in the field of computer science: these courses cover a broad range of strategic areas at the intersection of advanced software development and HPC. They provide in-depth knowledge on the design and optimisation of distributed systems, real-time data analysis, predictive modelling, cybersecurity, and performance evaluation. The offering is completed by courses on big data management and artificial intelligence, equipping you with the skills to develop scalable, intelligent, and secure solutions in complex HPC environments.

20 ECTS in multidisciplinary applications: these courses give you the opportunity to directly explore how HPC is applied across a variety of scientific, engineering, and technological domains. The goal is to demonstrate how the skills you acquire can be used to solve complex, real-world problems, fostering a hands-on, solution-oriented approach to interdisciplinary challenges. Areas of application include:

  • aerospace, computational fluid dynamics, and mechanical engineering, focusing on the simulation of complex physical systems;
  • energy modelling and sustainability, for the analysis and optimisation of energy and environmental systems;
  • genomica e bioinformatica computazionale, dove l’HPC è essenziale per l’elaborazione e l’analisi di grandi moli di dati biologici;
  • computational finance, for market scenario simulation, time series analysis, and risk assessment;
  • computer science as an application domain of HPC, for example in the development and training of AI models, natural language processing, or hardware/software co-design in systems optimised for large-scale algorithm execution.

In all these contexts, HPC acts as a key enabler for compute-intensive applications that demand high processing power, parallelism, and scalability. Exposure to real-world application domains helps you consolidate methodological skills while tackling practical, complex problems.

Thesis Project and and Final Exam

The study programme concludes with 20 ECTS dedicated to your thesis project, which may take an applied, innovative, or research-oriented approach. This is a valuable opportunity to put your skills into practice, contribute to cutting-edge projects in the HPC field, and engage with real-world problems and high-complexity challenges. The final assessment consists of writing, presenting, and defending your thesis.

The thesis project is agreed upon with a faculty member, who will act as your supervisor, guiding you in defining the topic, planning the work, and developing the project, while providing scientific and methodological support.

You may also carry out your thesis in collaboration with companies, supercomputing centres, or research institutions. In some cases, the thesis project can be combined with a voluntary curricular internship, provided it is agreed upon and supervised by a faculty advisor.

On the website of the School of Industrial and Information Engineering, under the Bachelor's and Master's degree exams page, you can find:

  • the Master of Science graduation exam regulations and the supplementary regulations for each Study Programme;
  • information on the procedures for graduation sessions, deadlines, and instructions for thesis submission;
  • thesis format templates: templates for both the traditional thesis format and the article-style format, as well as the executive summary template (which must be submitted along with the thesis in case of a thesis with a second reviewer).

Multidisciplinarity in action

Project-based activities integrated into the various courses are a distinctive feature of the HPC Engineering study course and play a key role both in the learning process and in the assessment. Throughout the two years, you will often work in teams with your peers on real-world problems or complex simulations, applying the skills and knowledge you have acquired.

A particularly enriching aspect of the HPCE programme is the diverse composition of both project teams and the class as a whole, bringing together students from a variety of backgrounds — such as mathematics, computer science, physics, and other engineering fields. This multidisciplinary environment allows you to share complementary skills and strengths, fostering peer-to-peer learning and the development of effective teamwork. You will learn to communicate across disciplines and collaborate in interdisciplinary settings — essential skills for both research and industry.