Advanced Applied Deep Learning in Physics and Engineering
Course, Master's level, 1FA006
Expand the information below to show details on how to apply and entry requirements.
Spring 2026 Spring 2026, Uppsala, 33%, On-campus, The course will be taught in English, if needed
- Location
- Uppsala
- Pace of study
- 33%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 23 March 2026–7 June 2026
- Language of instruction
- The course will be taught in English, if needed
- Entry requirements
-
120 credits in science/engineering. Applied Deep Learning in Physics and Engineering. Proficiency in English equivalent to the Swedish upper secondary course English 6.
- Selection
-
Higher education credits in science and engineering (maximum 240 credits)
- Fees
- If you are not a citizen of a European Union (EU) or European Economic Area (EEA) country, or Switzerland, you are required to pay application and tuition fees.
- First tuition fee instalment: SEK 12,083
- Total tuition fee: SEK 12,083
- Application deadline
- 15 October 2025
- Application code
- UU-63147
Admitted or on the waiting list?
- Registration period
- 9 March 2026–22 March 2026
- Information on registration from the department
About the course
In this course, you will delve into advanced concepts in neural networks and deep learning. You will explore techniques such as Graph Neural Networks, Generative models, quantized networks, and more, along with practical skills in using tools like TensorFlow, PyTorch, and JAX. These topics will be illuminated with examples from current research in physics and technology. Upon completion of the course, you will be able to design custom neural network architectures for problems in physics and technology, handle complex datasets for training, and choose the right deep learning tools for different problems, making you ready for advanced applications in these fields.
Contact
- Study counselling
- studievagledare@physics.uu.se
- +46 18 471 35 21