The summer school consists of four teaching blocks, each focusing on a specific topic and taught by a different lecturer and a preliminary teaching block (3 hours) on Tuesday 9th of June for a short introduction on machine learning, an alignment abacus to allow students to follow subsequent advanced courses. This will be in blended format, the lecturer in presence at the Department of Mathematics. Each of the other 4 blocks (6 hours each) is complemented by a lab session (2 hours) and/or exercise session (2 hours). To complements these sessions, seminars given by experts in imaging and covering a wide range of applications will be organized. The final blended session with exams will be organized two weeks after the conclusion of the school. The total number of hours will be 42 split over two consecutive weeks.
A certificate of attendance and number of 4 ECTS will be assigned.
The Summer School is designed for PhD students as well as early-career researchers (e.g., postdoctoral fellows) with a background in applied mathematics, computer science, engineering, or physics. Particular attention will be paid to ensuring equal access to the Summer School, without discrimination based on sex, ethnicity, religion, or income. If selection is required due to classroom capacity limitations, it will be based primarily on merit and on the applicants’ interest in the topics covered by the Summer School.