Introduction
Project work focusing on the candidates research area.
Content
The project course will contain topics at advanced level in signal and image processing, machine learning, power systems and cybernetics. The course is intended to bring the student to the research front in an area pertaining to his/her PhD project.
Learning outcome
After completed the course, the student shall have sufficiently detailed knowledge of the topic to be at the research front within the field and will be able to perform through research and development within the specific topic.
Required prerequisite knowledge
None
Exam
Project
Weight 1/1
Marks Passed / Not Passed
Examination form depends on type of PhD project.
Coursework requirements
Oral presentation
Method of work
8 hours of project work per week. There are no ordinary lectures in this course; each student will receive
independent supervision from the selected supervisor.
Using AI to support learning and study activities
AI tools may be used in ELE920 as a learning aid, but must never substitute for the research-level depth the course builds — being at the research front of your PhD topic and owning the academic work yourself is precisely what you must master. Productive uses include asking AI to explain a concept (e.g., how a research approach is chosen, what defines the research front in your field, or how a result is critically assessed), to gather background and references to inform your own work, or to improve the structure and language of work you have written yourself. Any use of AI must always be disclosed in your submission as specified in the syllabus, and you remain responsible for critically evaluating whatever the AI produces.
Open for
Students that follows the PhD-programme i Information Technology, Mathematics and Physics.
Admission requirements
Course assessment
The faculty decides whether early dialogue will be held in all courses or in selected groups of courses. The aim is to collect student feedback for improvements during the semester. In addition, a digital course evaluation must be conducted at least every three years to gather students’ experiences.
The course description is retrieved from FS (Felles studentsystem). Version 1