Master of Science in Data-Centric Engineering

In the master’s degree program 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 program, you will learn about computational techniques and artificial intelligence for analyzing and modeling 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 modeling, and computer science for analyzing large datasets and methods for designing complex engineering systems. This includes uncertainty quantification and risk analysis, pattern recognition, and understanding and modeling interconnections of large-scale systems;
• competencies 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.

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

The program includes core, advanced specialization and elective studies.

Core studies will provide a solid background in computational techniques and the ability to apply the methods to different scientific, industrial and financial problems, such as climate change studies, vision-based quality control and process engineering.

In advanced specialization studies, you will concentrate on:

• applied mathematics or
• computer vision and pattern recognition.

In applied mathematics, you will study numerical analysis, statistical methods, inverse problems, and modeling and simulation. In computer vision and pattern recognition, topics include image processing/analysis and deep learning with applications in, for example, medical engineering and industrial machine vision.

Elective studies can be chosen from any discipline at LUT to complement your studies.

The Master’s thesis, 30 ECTS credits, is an advanced research project completed during the fourth semester. The Master’s thesis requires approximately six months of full-time work.

Double degree studies for students from partner universities

Students from partner universities are able to study as double degree students at LUT. Students admitted to the double degree program receive a degree certificate from two universities after they have fulfilled the degree requirements of both universities.

The program’s double degree partner universities are:

• Università della Svizzera italiana, Switzerland
• University of Koblenz-Landau, Germany
• Brno University of Technology, Czech Republic
• The Politecnico di Milano, Italy
• Peter the Great St. Petersburg Polytechnic University, Russia
• South Ural State University, Russia
• Southern Federal University, Russia
• African Institute for Mathematical Sciences, Rwanda

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 program 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 the following:

• 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 modeling and simulation in industry R&D
• different positions at universities and research institute at all career levels from junior researchers to professors.

Lucrative start-up companies founded by our alumni contribute to

• sawmill industry digitalization
• computer vision for the industry
• environmental monitoring.

The admission requirements for the program can found on the university page.

Further information about the tuition fee and available scholarships for the program can found on the university page.