Overview
- Educational areaComputer Engineering and AI
- DegreeMaster of Science, MSc
- LanguageEnglish
- Duration2 years
- Rate of studyFull-time, 100 %
- Instructional timeDaytime
Important dates
- Application opens
- Application closes
Summary
Combine rigorous technical foundations with a high-level understanding of the societal implications of AI. With a focus on key areas like science and health, language and cognition models, and machine learning, you will be ready to develop intelligent, innovative systems that are complex, powerful, and secure.
Specialisations
Programme description
AI is increasing in importance across virtually all sectors: healthcare, communications, transport, manufacturing, finance, scientific research. There is a huge demand across all these industries for engineers who understand the underlying technology and the wider consequences of its use.
You will begin with a core foundation in machine learning, mathematics and algorithms. This ensures a deep understanding of how modern AI systems are designed, trained, and deployed, building the analytical skills needed to understand their strengths and limitations.
The work is heavily project-based, giving you the chance to apply what you are learning to relevant, real-world scenarios. This means using AI methods to tackle open-ended problems while developing skills in teamwork, communication, and collaboration with students from other disciplines. Many projects involve external stakeholders from relevant organisations like the Volvo group and AstraZeneca, combining technical expertise with critical reflection on topics such as interpretability, ethics, fairness, and societal impact.
There is a wide range of elective courses and specialisations that allow you to deepen your expertise. These reflect some of the most active and rapidly developing areas of AI today, covering advanced mathematics and machine learning, language technologies and reasoning systems, and AI applications in healthcare and scientific research. Together, they represent the breadth and depth of the field, offering the flexibility to tailor the studies towards your own specific interests and ambitions.
Responsible AI is also a recurring theme throughout. Alongside technical skills, you will develop an awareness of the legal and societal dimensions of artificial intelligence, exploring how it can support the growth of sustainable and democratic societies.
The programme is ideal for students from a range of scientific and engineering backgrounds and creates opportunities to work across disciplinary boundaries. This reflects the reality of modern AI development, where advances can require contributions from computer science, mathematics, engineering, healthcare, and the natural sciences.
Job opportunities
After graduation, you will be well-prepared for a wide range of roles across research, industry, and the public sector. The combination of technical depth, project experience, and ethical awareness will prepare you to contribute to many different organisations developing and applying advanced AI technologies.
Typical career paths include AI and machine learning engineering, data science, software development, systems architecture, or research careers.
Chalmers has strong connections to industry and research partners working with AI applications. These include Volvo Group, Ericsson, AstraZeneca, and many more in Gothenburg and Sweden. You will have opportunities to make contacts and work alongside representatives from organisations like these, through projects, internships, and potential collaboration on your master's thesis.
The education also provides an excellent foundation for doctoral studies in artificial intelligence, machine learning, computer science, mathematics, and related disciplines.
Research
Chalmers has active research projects in AI and many of the courses in the programme are taught by researchers at the forefront of their fields. This ensures that you are always up-to-speed with contemporary developments in this fast-moving field.
Research areas represented include machine learning theory, reinforcement learning, natural language processing, neuro-symbolic AI and algorithmic theory. There is also a focus on applied AI in areas like healthcare, transportation, and the natural sciences, through groups like the Healthy AI lab, and the AI Lab for Molecular Engineering. You will also benefit from connections to research in computer vision, quantum computing, and robotics, including groups such as the Wallenberg AI, Autonomous Systems and Software Program
This research spans several departments at Chalmers, and is also linked to several of the university's ‘Areas of Advance’, in Information and Communication Technology and Health Engineering. The Areas of Advance are the largest research networks at Chalmers, covering broad themes that link many disciplines.
You will have the chance to complete your master's thesis with active research groups like these, as well as external organisations. Your master’s thesis can provide a direct contribution to ongoing research, or in industrial innovation through these contacts. It also provides a natural pathway into further studies at doctoral level.
Requirements
Bachelor's degree in: Science, Engineering, Technology
Prerequisites: Mathematics (at least 30 cr. including Multivariable Analysis, Linear Algebra, Mathematical Statistics), Programming (at least 6 cr. in a general-purpose language e.g., C, C++, Python, Java, Haskell or similar), Algorithms and/or Data Structures (at least 6 cr.), at least 6 cr. Data science/AI/Machine learning
English language requirements: Prove your English proficiency
How to apply
From application to admission
This guide explains how to apply for a Master's programme and which documents you need to submit to complete your application.