Advanced Applied Deep Learning in Physics and Engineering

Education facts

Code: 1FA006

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.

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.

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

• Advanced Applied Deep Learning in Physics and Engineering

  Uppsala University

  Uppsala

  Start date: 22 March 2027

  End date: 6 June 2027

  Pace of study: 33 %