Degree
Master of Science in Technology
Study field
Technology
School
LUT School of Engineering Science
Credits
120 ECTS credits
Duration
2 years
Language
English
Form of study
Contact teaching
Location
Lappeenranta
When to apply
Regular admission
1 December 2022–18 January 2023
Rolling admission
1 November 2022–15 May 2023
Tuition fee
EUR 13 500/academic year (non EU/EEA students). Early bird and scholarships available.
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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.

In this programme, 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.

Study objectives

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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.

Degree structure and studies

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The Master's Programme in Data-Centric Engineering is a two-year programme. It leads to the degree of Master of Science in Technology, M.Sc. (Tech.), which is 120 ECTS credits.

The programme includes advanced specialisation, minor and elective studies as well as a Master's thesis.

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Career prospects

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Data-centric engineering provides a solid stepping-stone to a career in academia, government, industry, or finance, as well as entrepreneurial skills for establishing a start-up company.

The skills and competencies gained in the programme prepare you for continual learning and for the ongoing transition from software-intensive to data-intensive engineering work. Practical examples of our graduates' careers include various positions in artificial intelligence companies; for example, developing computer vision and virtual reality, enhancing the capability of medical imaging in medical engineering companies, working on modelling and simulation in industry R&D, different positions at universities and research institutes at all career levels from junior researchers to professors.

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Lucrative start-up companies founded by our alumni contribute to sawmill industry digitalisation, computer vision for industry and environmental monitoring.

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