The new master program in Scientific and Data Intensive Computing (class LM 44), starting from September 2023, is an international master program, taught in English, focussed on High Performance Computing, Data Engineering, Scientific Computing, Quantum Computing, with particular attention to the convergence of Machine Learning and Computer Simulation for Digital Twins. Several application areas are available, including computational mechanics, ocean and climate, computational physics and chemistry, collective adaptive systems (e.g. smart cities).

The new master program in Scientific and Data Intensive Computing is jointly organised with the University of Udine, with the participation of SISSA (International School for Advanced Studies), ICTP (International Centre for Theoretical Physics), Area Science Park (Scientific and Technologic park), INAF (National Institute of Astrophysics), and OGS (National Institute of Oceanography and Geosciences). It also features the collaboration and support of some companies.

The program is organised in three curricula:

- Computational Modeling and Digital Twins
- High Performance Computing and Data Engineering
- Quantum Computing

### Admission Requirements

- academic degree, or equivalent, with a graduation grade greater or equal to 80 over 110. In the absence of a Bachelor Degree, the weighted average of the exams will be considered instead.
- obtained at least 60 ECTS in total in the following topics:
- mathematics (MAT/*)
- computer science (INF/01)
- information engineering (ING-INF/*)
- industrial engineering (ING-IND/*)
- civil engineering (ICAR/01-09)
- physics (FIS/*)
- statistics and mathematical methods for decisions (SECS-S/*)
- economy and finance (SECS-P01, 05, 08, 09, 11)
- chemistry (CHIM/*)
- molecular and genetic biology (BIO/10, 11, 18)
- geophysics and terrestrial physics (GEO/10, 12)

- among the 60 ECTS above, at least at least 21 ECTS must be obtained in maths (MAT/*, SECS-S/06)
- among the 60 ECTS above, at least at least 6 ECTS must be obtained in computer science and engineering (INF/01, ING-INF/05)
- adequate knowledge of English, certified by internationally recognized certification of level B2 or higher, or by passing a test equivalent to level B2.

## Curricula

Here we will provide a brief overview of the three curricula. More details will be available soon in a dedicated website.

All curricula share a common set of core courses, giving the necessary knowledge in the areas of computer science and HPC, computational modelling, statistics and machine learning. Each curriculum has some specific core course and electives. For some courses, students have to choose between two options: typically, one course provides also introductory material (marked with * below) - for students who never studied the subject - while the other one treats more advanced topics, assuming a basic knowledge of the subject - for students that had already courses on that specific subject. Each student will have to choose one of the two courses. This will permit to assemble a personalised study plan depending on the background.

### Computational Modeling and Digital Twins

This curriculum has several focusses or specialisations: Computational Mechanics, Computational Physics and Chemistry, Computational Cosmology, Discrete modelling, Computational Modelling of Ocean and Climate. More details on these specialisations (corresponding to specific choices of courses below) will be made available in the new website of the master program.

*I Year*

Advanced programming (*) *or* Software Development Methods (6 ECTS)

Introduction to High Performance Computing (6 ECTS)

Introduction to Cloud Computing (6 ECTS)

Probability and Statistics for Scientific Computing (6 ECTS)

Algorithms for Scientific Computing (*) *or* Advanced Algorithms for Scientific Computing (6 ECTS)

Introduction to Machine Learning (*) *or* Probabilistic Machine Learning *or* Reinforcement Learning (6 ECTS)

Deep Learning (6 ECTS)

Numerical Analysis (*) *or* Stochastic Modelling and Simulation *or* Global and Multi-Objective Optimization (6 ECTS)

Advanced Numerical Analysis *or* Stochastic Modelling and Simulation (6 ECTS)

Core Elective Course (group A) (6 ECTS)

*II Year*

Advanced Topics in Scientific Computing *or* Simulation Intelligence and Learning in Autonomous Systems (6 ECTS)

Core Elective Course (group A or B) (6 ECTS)

Two Elective Courses (12 ECTS)

Internship (12 ECTS)

Thesis (24 ECTS)

*Core Electives - group A *- each course has 6 ECTS

Modelling and Control of Cyber-Physical Systems I

Probabilistic Machine Learning

Reinforcement Learning

Mathematical Optimization

Computational Fluid Dynamics

Remote Sensing

Introduction to Astrophysics and Cosmology

Computational Physics Laboratory

Computational Quantum Chemistry

*Core Electives - group B * - each course has 6 ECTSPhysics and modelling of turbulence

Marine Ecosystems Modelling and Analytics

Galaxy Astrophysics

Advanced Cosmology

Statistical Thermodynamics

Image Processing in Physics

Computational Solid Mechanics

Computer Vision and Pattern Recognition

*Electives* - each course has 6 ECTS

All core electives not taken before

Computational Climatology

Quantitative Ecology

Information Retrieval and Data Visualisation

Advanced High Performance Computing

High Performance Computing and Data Infrastructures

Advanced Deep Learning and Kernel Methods

Data Management

Bayesian Statistics

Unsupervised Machine Learning

GPU and Parallel Programming

Machine Learning Operations

Software Development Methods

Modelling and Control of Cyber-Physical Systems II

Artificial Intelligence for Cyber-Physical Systems

Numerical Methods in Quantum Mechanics

Radiative Processes

Environmental Fluid Mechanics

Molecular Simulation

Computational Methods in Particle Physics

### High Performance Computing and Data Engineering

This curriculum has two focusses or specialisations: HPC and Data Engineering. More details on these specialisations (corresponding to specific choices of courses below) will be made available in the new website of the master program.

*I Year*

Advanced programming (*) *or* Software Development Methods (6 ECTS)

Introduction to High Performance Computing (6 ECTS)

Introduction to Cloud Computing (6 ECTS)

Probability and Statistics for Scientific Computing (6 ECTS)

Algorithms for Scientific Computing (*) *or* Advanced Algorithms for Scientific Computing (6 ECTS)

Introduction to Machine Learning (*) *or* Unsupervised Machine Learning (6 ECTS)

Deep Learning (6 ECTS)

Numerical Analysis (*) *or* Probabilistic Machine Learning *or* Mathematical Optimization (6 ECTS)

Mathematical Optimization or Advanced High Performance Computing (6 ECTS)

Data Management (*) or Advanced Data Management (6 ECTS)

*II Year*

High Performance Computing and Data Infrastructures (6 ECTS)

Core Elective Course (6 ECTS)

Two Elective Courses (12 ECTS)

Internship (12 ECTS)

Thesis (24 ECTS)

*Core Electives *- each course has 6 ECTS

Advanced Data Management

Advanced Database Systems

Machine Learning Operations

Information Retrieval and Data Visualisation

Computer Vision and Pattern Recognition

*Electives* - each course has 6 ECTS

All core electives not taken before

GPU and Parallel Programming

Natural Language Processing

Stochastic Modelling and Simulation

Advanced Deep Learning and Kernel Methods

Artificial Intelligence for Cyber-Physical Systems

Bayesian Statistics

Explainable and Reliable Artificial Intelligence

Software Development Method

### Quantum Computing

*I Year*

Advanced programming (*) *or* Software Development Methods (6 ECTS)

Introduction to High Performance Computing (6 ECTS)

Introduction to Cloud Computing (6 ECTS)

Probability and Statistics for Scientific Computing (6 ECTS)

Algorithms for Scientific Computing (*) *or* Advanced Algorithms for Scientific Computing (6 ECTS)

Introduction to Machine Learning (*) *or* Information Theory (6 ECTS)

Deep Learning (6 ECTS)

Probabilistic Machine Learning *or* Stochastic Modelling and Simulation (6 ECTS)

Introduction to Quantum Mechanics and Computing (6 ECTS)

Introduction to Quantum Information Theory (6 ECTS)

*II Year*

Two Core Elective Courses (12 ECTS)

Two Elective Courses (12 ECTS)

Internship (12 ECTS)

Thesis (24 ECTS)

*Core Electives *- each course has 6 ECTS

Information Theory

Quantum Algorithms

Quantum Machine Learning

*Electives* - each course has 6 ECTS

All core electives not taken before

Quantum Informatics and Software

Advanced Deep Learning and Kernel Methods

Data Management

Bayesian Statistics

Software Development Methods

Explainable and Reliable Artificial Intelligence