Data-Centric Engineering | Master of Science in Technology

The current decade will see major advances in data-driven engineering and sciences, impacting all aspects of science, engineering, industry, and society via automation, artificial intelligence, robotisation and digital platforms. To develop innovative solutions to the world's most challenging problems, it is increasingly important to properly understand data and modern information processing methods.

The Master's Programme in Data-Centric Engineering is based on blending applied mathematics with computer science and engineering. During your studies, you will learn about artificial intelligence and machine learning and how mathematics and statistics form their basis. This will enable you to understand data and modern modelling and analysis methods, such as deep neural networks, profoundly and apply them to problems with societal impact. You will specialise in either Applied Mathematics, or Computer Vision and Pattern Recognition.

The programme is designed for students with a BSc degree in mathematics, applied mathematics, statistics, computer science, artificial intelligence, or the like.

During your studies, you will have extensive possibilities for international experiences, such as Erasmus exchange and double degree programmes with partner universities. In double degree programmes, you will get MSc degrees from both LUT and its partner university.

What will you learn in the programme?

In the Masters's Programme in Data-Centric Engineering, you will study at the intersection of applied mathematics, computational statistics and artificial intelligence.

These domains are changing all areas of work, including traditional industrial work, white-collar jobs, and health care. You will obtain a solid basis for research, development, innovation and entrepreneurship.

In this programme, you will learn about computational techniques and artificial intelligence for analysing and modelling data.

As a graduate, you will have:

• knowledge about computer science and engineering, machine learning, applied mathematics, and computational statistics;
• skills in computational techniques, mathematical modelling, and computer science for analysing large datasets and methods for designing complex engineering systems. This includes uncertainty quantification and risk analysis, pattern recognition, and understanding and modelling interconnections of large-scale systems;
• competences in the theory and practice of data-centric engineering that will enable you to work on multidisciplinary and interdisciplinary projects on, for example, satellite remote sensing or medical imaging;
• a professional network including connections to international research groups at leading universities, large industrial and financial companies, and start-ups.