Master's Programme in Machine Learning, Sensors and Systems, 120 credits
Start of studies
Autumn 2026
Form of education
Campus, Day-time, Full-time
Language
English
Tomorrow’s technical solutions require expertise across multiple disciplines. This Master’s programme brings together electrical engineering and computer science – two fields that are essential for developing sustainable, smart systems in an increasingly connected world.
You will gain the skills to work with complex technical systems, where machine learning and sensors interact to create intelligent solutions in areas such as energy, communication, transport, and healthcare.
What will you study?
The programme provides in-depth knowledge in both data-driven and model-based technological development. You will study subjects such as:
- machine learning and AI
- signal processing
- control engineering
- mechatronics and robotics
- sensor systems
- statistics and optimisation
You will learn to combine these areas of expertise to analyse problems from different perspectives and select appropriate solutions – sometimes using advanced AI, at other times simpler model-based techniques.
During the programme, you will take part in a project course where theory meets practice. Your studies will conclude with a Master’s thesis, which can be carried out in either electrical engineering or computer science, depending on your chosen specialisation.
After graduation – what are your career opportunities?
This is a distinctly engineering-focused programme, developed in collaboration with industry partners working in both research and application. You will gain the competence to work at the intersection of systems engineering and AI – a field with a current skills gap and rapidly growing demand.
After graduation, you may work with:
- developing sensors and intelligent systems
- optimising technical processes using AI and data-driven analysis
- working as a systems engineer, AI specialist, or research engineer
You may find employment in sectors such as:
- energy systems and environmental technology
- communication and infrastructure
- autonomous vehicles and robotics
- medical and healthcare technology
The programme also provides a solid foundation for pursuing doctoral studies.
Please contact the Admissions Office with any questions regarding the entry requirements.
The tuition fee is SEK 70,000 per semester. One semester corresponds to 30 ECTS credits. EU/EEA citizens are not required to pay fees.
BTH offers a scholarship programme for both prospective students and current students. If you are a female citizen of one of ten eligible countries and you apply for this program as your first choice, you can also apply for the Swedish Institute Scholarship Pioneering Women in STEM for prospective students. Learn more about scholarships.
Do you have any questions about the programme? Please send them to us using the form on the International Student Guide.
"I love that this programme offers a mix between theory and practice. The labs and real projects allow me to put what I learn and develop valuable skills for my future career."
Motei Shaban - student
The Master’s programme in Machine Learning, Sensors and Systems equips you with advanced technical expertise, focusing on intelligent systems. The programme combines subjects from electrical engineering and computer science – a combination that is becoming increasingly important as technology grows more complex and integrated.
You will develop your ability to analyse, understand and build systems where AI, sensors and control engineering interact – skills in high demand across sectors such as energy, robotics, communication, and healthcare.
Year 1 – Foundations and specialisation
The first year provides a solid foundation in the technologies used to build intelligent systems. You will take courses in:
- signals and systems
- control theory
- statistics and time series analysis
- artificial intelligence and deep learning
Depending on your interests and future goals, you will choose courses that align with a focus on either electrical engineering or computer science. For example, you may specialise in optimisation, electromagnetic field theory, or advanced machine learning.
Year 2 – Application, specialisation, and research
In the second year, the focus shifts towards practical application and research-oriented learning. Courses include topics such as:
- sensor systems
- computer vision
- robotics
- research methodology
You also have the opportunity to further specialise within your chosen field, for example through courses in radar systems (for electrical engineering) or AI system security (for computer science).
Master’s Thesis – Expertise and collaboration
The programme concludes with a Master’s thesis, giving you the opportunity to:
- specialise in a chosen subject area
- apply your knowledge in a real-world project
- collaborate with researchers or industry partners
The thesis can be part of an ongoing research project and gives you the chance to demonstrate your skills in practice – whether your goal is a career in industry or further academic research.
Note! The course list is tentative. See the programme syllabus for an established course list.
* Elective course
Autumn semester 2026
Signals and Systems, basic course, 6 credits
Introductory Course in Machine Learning, Sensors and Systems, 3 credits
Applied artificiell intelligens, 6 credits
Introduction to Bayesian Statistics, 3 credits
Signals and Systems, continuation course, 6 credits
Electromagnetic Field Theory, 6 credits
Spring semester 2027
Project Course in Data Analysis, 6 credits
Time Series and Predictive Analytics, 6 credits
Advanced Machine Learning, 6 credits
Automatic Control, advanced course, 6 credits
Deep Machine Learning, 6 credits
Applied Machine Learning, 6 credits
Autumn semester 2027
Security in AI Systems, 6 credits
Research Methodology in Computer Science, 6 credits
Mechatronics including robotics, 6 credits
Research Methodology for Engineers, 6 credits
Spring semester 2028
Students who apply for a course or programme, and meet the general and specific entry requirements, compete with one another for available places. When there are more qualified applicants than there are places for an education, the places are distributed through a selection. Read about the selection methods and procedure here.
Evaluations and advisory board
The study programmes at BTH are continuously monitored and developed through yearly follow-up dialogues, course evaluations after each completed course, and programme evaluations. Results from follow-ups and evaluations can lead to changes in the programmes. These changes are always communicated to the students.
Each educational programme is tied to an advisory board that discusses issues such as the quality of the programme, its development, and relevance for the labour market. In the advisory board, or a committee to the advisory board, teachers, external members, students and alumni are represented.
Frequently asked questions
Bachelor's Degree or Bachelor of Science in Electrical Engineering, or in other field of Technology/Science. Mathematics, 15 credits, a course in Mathematical Statistics, a course in Control Theory/Control Engineering, and a course in Programming. English 6.
The track Electrical Engineering (ELEC) also requires a course in Electrical Circuits/Circuit Theory.
The track Computer Science (COMP) also requires a course in Data Structures and Algorithms.
