The Course

  • Why choose this course?

    The Master in Scientific and Data-Intensive Computing offers interdisciplinary knowledge, preparing professionals for emerging fields like digital twins, high-performance computing, data analysis, and quantum computing. Courses are taught in English and involve institutions like SISSA, ICTP, Area Science Park, OGS, and other local research centers.

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  • Educational objectives

    The program covers advanced topics such as programming, algorithm design, high-performance computing, data management, and quantum computing. Students gain expertise in computational methods for large-scale data analysis, machine learning, and mathematical modeling. Core courses are complemented by specialized electives.

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  • Career prospects

    Graduates can work in High-Performance and Data-Intensive Computing, with strong skills in data-driven modeling, design, and evaluation of HPC systems in parallel and distributed environments. They can also work in Simulation-based Science and Engineering, specializing in scientific computing, numerical modeling, optimization, and data analytics.

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  • Final examination and degree

    At the end of the program, the University awards a master degree with a grade out of 110, valid at the national level and recognized internationally, certifying the skills acquired. The final project consists of an original thesis, guided by a supervisor, in which the student addresses a real-world problem proposed by a research lab or company.

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Why choose this course?

The Master’s degree programme in Scientific and Data-Intensive Computing has been established to prepare professionals capable of addressing key challenges of the digital society in areas such as computational science and engineering, digital twins, high-performance computing, and data-intensive computing. By integrating classical modelling knowledge with expertise in high-performance computing and modern methods for data management and analysis, graduates can find employment across a wide range of sectors, including research, development and design centres—both public and private—technological, industrial and scientific domains, computing laboratories, companies providing services for the processing of large volumes of data, financial, banking or insurance institutions, service companies, and independent consultancy firms.

The programme provides students with a solid methodological foundation in three areas: data analysis and machine learning; mathematical and computational modelling; and computer science, with particular emphasis on intensive and distributed computing.
 Students will deepen their competences in certain foundational aspects according to the chosen curriculum, and may then complement their training with modules in applied areas of the physical and natural sciences and engineering. Students will acquire not only theoretical knowledge but also the ability to apply it to the solution of practical problems through individual exercises and group projects. Training will be complemented by seminar courses and by an internship/placement and a dissertation, which may be undertaken with partner companies and research organisations.

The programme is international, taught in English, and is organised by the Universities of Trieste and Udine, in collaboration with SISSA, ICTP, Area Science Park and other research institutions in the Trieste area and in the Friuli Venezia Giulia (FVG) Region.

Educational objectives

Knowledge and Understanding

Computer Science and High-Performance Computing
 The modules offered during the programme will provide students with a solid preparation in Information and Communication Technologies and Computer Science, in particular:

  • Advanced programming, software engineering, algorithm design.
  • Fundamentals of intensive computing, both hardware and software; parallel programming; cloud computing.
  • Management of large volumes of data, at both database and hardware architecture levels.
  • New computing paradigms for high-performance computing, such as quantum computing.

A core of the above knowledge will be acquired in all curricula, with any further specialisation entrusted to optional modules or curriculum-specific modules.

The teaching methods used to achieve these objectives are classroom lectures and practical exercises in the computer laboratory. Series of seminars and additional laboratory exercises will allow further refinement of this knowledge and its connection with the needs of industry and services.

Knowledge will be assessed through examinations that evaluate both theoretical understanding and the ability to apply such knowledge to specific problems, including by means of individual and group projects.

Data Analytics and Machine Learning
 The modules offered during the programme will provide students with a solid preparation in computational and statistical methods for extracting information from and analysing large volumes of data and their applications:

  • Fundamental techniques of machine learning and inferential statistics.
  • Advanced statistical modelling.
  • Advanced techniques in statistical machine learning.
  • Artificial intelligence techniques based on machine learning, such as deep learning.
     The above knowledge will be acquired in part in all curricula, with greater emphasis in curricula oriented towards data-intensive computing.

The teaching methods used to achieve these objectives are classroom lectures and practical exercises in the computer laboratory, including the application of the methodologies learned to concrete case studies. Series of seminars and additional laboratory exercises will allow further refinement of this knowledge and its connection with the needs of industry, services and the research sector.

Knowledge will be assessed through examinations that evaluate both theoretical understanding and the ability to apply such knowledge to specific problems, including by means of individual and group projects.

Modelling and Simulation
 The modules offered during the programme will provide students with a solid preparation in the construction of mathematical models and in their analysis and training through computational and statistical methods:

  • Techniques of numerical analysis and the simulation of equations.
  • Stochastic modelling, simulation, parameter estimation.
  • Optimisation techniques for convex and non-convex problems, and combinatorial optimisation.
  • Advanced methods for the numerical solution of differential equations.
  • Mathematical foundations of modelling and automatic control.
  • Advanced methods integrating mathematical-numerical modelling and machine learning.

The above knowledge will be acquired in part in all curricula, with greater emphasis in curricula oriented towards modelling and simulation.

The teaching methods used to achieve these objectives are classroom lectures and practical exercises in the computer laboratory, where the methods studied will be implemented—also using scientific-computing libraries—and tested on concrete examples. Series of seminars and additional laboratory exercises will allow further refinement of this knowledge and its connection with the needs of industry, services and research.

Knowledge will be assessed through examinations that evaluate both theoretical understanding and the ability to apply such knowledge to specific problems, including by means of individual and group projects.

Multidisciplinary Knowledge and Applications
 Students will learn or strengthen their knowledge in one or more application domains in engineering or science:

  • Fluid dynamics and computational mechanics: theoretical foundations, computational methods, applications to industrial and scientific problems, with particular reference to the earth sciences.
  • Computational Physics and Chemistry: computational methods for physics and chemistry, with particular reference to theoretical chemistry, materials science, condensed-matter physics, biophysics and computational cosmology.
  • Quantum computing: mathematical and computational foundations of quantum communication and information.
  • Geosciences, climatology and oceanography: computational methods for data analysis and prediction in the earth sciences, geophysics, oceanography and climatology.
  • Smart cities, smart transportation, smart industry: computational and data-driven methods for the modelling and optimisation of intelligent systems in industry, transport, logistics and smart cities.

The above knowledge will be acquired in cognate modules, and where appropriate in elective modules, available within the various curricula. The choice of any specialisation area is left to the student, with the support of ongoing academic guidance.

The teaching methods used to achieve these objectives are classroom lectures and practical exercises in the computer laboratory on relevant scenarios and problems. Series of seminars and additional laboratory exercises will allow further refinement of this knowledge and its connection with the needs of industry and services.

Knowledge will be assessed through examinations that evaluate both specific knowledge of the application domains and the ability to apply mathematical, statistical and computational methodologies to solve specific problems in these domains, including by means of individual or group projects.

The teaching methods used to achieve these objectives are classroom lectures and practical exercises in the computer laboratory on relevant scenarios and problems. Series of seminars and additional laboratory exercises will allow further refinement of this knowledge and its connection with the needs of industry and services.

Knowledge will be assessed through examinations that evaluate both specific knowledge of the application domains and the ability to apply mathematical, statistical and computational methodologies to solve specific problems in these domains.

 

Ability to Apply Knowledge and Understanding

Computer Science and High-Performance Computing
 Graduates of both curricula will be able to implement algorithms and methods for solving complex problems and to identify and use the most suitable intensive-computing resources for a given problem. They will know how to write efficient code tailored to the computing resources available. They will be able to develop efficient algorithms, including for parallel computing. In the curricula more oriented towards data-intensive computing, they will be able to manage large databases by applying modern techniques of data structuring and data mining.

The teaching methods to achieve these goals include project activities—both individual and group—within the curricular modules, as well as the internship/placement and the final dissertation, where students will exercise their computational competences in solving a complex problem.

Data Analytics and Machine Learning
 Graduates will be able to use the methodologies learned in the curricular modules to solve concrete, highly complex problems in statistical modelling, in building predictive models, and in extracting information from large volumes of data. They will know how to interact with experts in multidisciplinary teams, identify the problem to be solved, and select the best tools for its solution.

The teaching methods to achieve these goals include project activities—both individual and group—within the curricular modules, as well as the internship/placement and the final dissertation, where students will exercise their statistical and computational competences for the analysis of large volumes of data.

Modelling and Simulation
 Graduates will be able to use the methodologies learned in the curricular modules to solve concrete, highly complex problems in mathematical modelling: building models, training them from data, and solving them numerically. They will know how to interact with experts in multidisciplinary teams, identify the problem to be solved, construct an appropriate model, and select the best tools for its solution.

The teaching methods to achieve these goals include project activities—both individual and group—within the curricular modules, as well as the internship/placement and the final dissertation, where students will exercise their mathematical-modelling competences on specific, high-interest problems.

Multidisciplinary Knowledge and Applications
 Graduates will develop an understanding of the basic problems in the application area considered. They will acquire competences for interacting with experts from different disciplines and for building a shared language for such interaction.
 They will develop skills and abilities in selecting and using, in an integrated and harmonised manner, mathematical models, statistical methods and intensive computing, with the aim of achieving effective solutions to the problem at hand, while respecting the constraints on the resources available.

The teaching methods to achieve these goals include project activities—both individual and group—within the curricular modules, as well as the internship/placement and the final dissertation.

Career Prospects

Employment and professional opportunities for graduates

Expert in High-Performance and Data-Intensive Computing

Graduates may perform high-responsibility roles in a wide variety of fields:

  • Companies/research centres operating in information processing and computational science and engineering;
  • Computing centres;
  • Companies and public bodies involved in managing large volumes of data (for example: the Revenue Agency, ISTAT, Poste Italiane, environmental agencies);
  • Public and private research and development laboratories;
  • Insurance companies and financial institutions;
  • Biomedical and pharmaceutical industries;
  • Consultancy firms.

Expert in Simulation-Based Science and Engineering
 Graduates may perform high-responsibility roles in a wide variety of fields:

  • Research, development and design centres—public and private—operating in information processing and computational science and engineering;
  • Computing centres;
  • Public and private research and development laboratories;
  • Computational engineering (simulation-based engineering);
  • IT companies producing scientific and technical software;
  • Consultancy firms.

 

Competences Associated with Each Role

Expert in High-Performance and Data-Intensive Computing

The expert in High-Performance and Data-Intensive Computing will develop a broad spectrum of scientific and technological competences, both theoretical and practical, enabling them to tackle professional challenges successfully.

In particular, the competences acquired in the Master’s degree in Scientific and Data-Intensive Computing enable the implementation of models of complex systems—e.g. digital twins—on high-performance computing systems through advanced algorithms, parallel and distributed programming (including the use of accelerators), numerical methods, machine learning algorithms, and methods for managing large quantities of data.

The graduate in Scientific and Data-Intensive Computing will be able to master classical and data-driven modelling techniques; analyse, evaluate and design high-performance computing systems and software in parallel and distributed environments. They will also be able to apply the scientific method to the design, implementation and evaluation of models of complex systems.

The graduate will also develop strong communication skills to interact with other professionals, understand the problems posed, and communicate the outcomes of their work, including in an international context. They will be capable of continually updating themselves on technological and methodological innovations and of proposing innovative and efficient solutions to the problems addressed.

Expert in Simulation-Based Science and Engineering

The expert in Simulation-Based Science and Engineering will develop a portfolio of theoretical competences and practical experience that will enable them to tackle professional challenges successfully.
 In particular, the expert in Simulation-Based Science and Engineering will have solid competences in scientific computing, numerical modelling and optimisation, as well as strong skills in parallel programming and in the use of different types of HPC resources and data analytics.
 These competences will constitute a solid conceptual basis for analysing complex problems and proposing solutions that effectively integrate these tools.
 The graduate will also develop strong communication skills to interact with other professionals, understand the problems posed, and communicate the outcomes of their work, including in an international context. They will be capable of continually updating themselves on technological and methodological innovations and of proposing innovative and efficient solutions to the problems addressed.

 

Role in the Workplace

Expert in High-Performance and Data-Intensive Computing
 Graduates in Scientific and Data-Intensive Computing are experts in the use of high-performance computing systems and in managing the large quantities of data required.

In particular, they will be experts in the creation, management and use of parallel systems such as accelerators for high-performance applications in digital-twin modelling and data analysis. They will be able to abstract and analyse real-world problems, integrating different technologies and competences; to contribute to the modelling of real systems and to their simulation, also making use of the analysis of large quantities of data. This professional profile therefore has an essential role in the evolution of the scientific and technological applications of parallel computing.

Expert in Simulation-Based Science and Engineering
 The explosion of digital technologies for data management and analysis and for intensive computing is giving strong impetus to the development of Simulation-Based Science and Engineering, which combines in-silico design and analysis with techniques of continuous data integration and optimisation to create digital twins of the life cycle of products and services.
 Graduates in Scientific and Data-Intensive Computing will be able to work in teams on the design of highly complex systems and models in the context of Simulation-Based Science and Engineering, integrating in-silico design and analysis with techniques of continuous data integration and optimisation, thanks to the development of problem-solving abilities and solid competences in mathematics, computer science and data analytics. They will be able to design algorithms and integrated systems for simulation and for integration with data for models of products and services, and to run them on high-performance computing systems. They will also be able to collaborate within research groups to apply these techniques to frontier problems in the physical and natural sciences.

Final examination and degree

Characteristics of the final examination

The final examination represents an essential moment in the educational pathway of the Master’s degree, as it will allow the candidate not only to apply some of the methodologies learned during the programme, but also to experience in practice the communicative and conceptual challenges underlying the modelling and analysis of a complex problem, in collaboration with sector experts. It will also be an essential opportunity to refine and internalise the idea of an integrated and flexible use of the computational, statistical and mathematical methodologies and technologies that underpin the programme of study.
 The final examination will consist of a dissertation, developed originally under the guidance of a supervisor, in which the candidate will address a problem proposed by a research laboratory or a company. The student must analyse the problem, formalise it, identify the most effective tools for solving it, and then present the results of the analyses clearly and intelligibly to a non-specialist audience.