Type of course

Admission requirements

PhD students or holders of a Norwegian master´s degree of five years or 3+ 2 years (or equivalent) may be admitted. PhD students must upload a document from their university stating that there are registered PhD students. This group of applicants does not have to prove English proficiency and are exempt from semester fee. Holders of a Master´s degree must upload a Master´s Diploma with Diploma Supplement / English PhD students at UiT The Arctic University of Norway register for the course through StudentWeb . External applicants apply for admission through SøknadsWeb.

Application code 9303.

All external applicants have to attach a confirmation of their status as a PhD student from their home institution. Students who hold a Master of Science degree, but are not yet enrolled as a PhD-student have to attach a copy of their master's degree diploma. These students are also required to pay the semester fee.

Course content

The course covers various aspects of green computing, particularly on the principles and applications of energy- and resource-efficient computing, including green AI, edge intelligence, and datacenter. It consists of two parts. The first part, "Principles of energy- and resource-efficient computing systems," considers various energy- and resource-efficient techniques across different system stack levels, including computer architecture, runtime systems, libraries, frameworks, and algorithms. The second part, "Leveraging green computing towards sustainable development," considers green computing applications to monitor and reduce the environmental impact of ICT and other segments such as energy.

The PhD level course's learning outcome includes the knowledge and skills needed to do research in green computing in terms of scientific projects.

Recommended prerequisites

Objectives of the course

Knowledge - the student has

• knowledge of design and implementation principles in energy- and resource-efficient computing systems
• knowledge of practical approaches and toolsets to develop energy- and resource-efficient computing systems
• knowledge of applications of energy- and resource-efficient computing systems, including Green AI and edge intelligence
• knowledge of the contemporary state of the art on energy- and resource-efficient computing systems
• knowledge needed to do research in green computing

Skills - the student can / has

• design, model, and analyze algorithms and protocols for energy efficiency
• utilize development environments and tools to develop energy-aware applications and systems towards sustainable development
• review advanced scientific papers and identify research problems and challenges in green computing
• skills needed to do research in green computing

General competence - the student know

• how to read scientific literature, carefully extract information from it, and present it coherently in public
• how to conduct technical reviews and come up with critiques to current solutions to open problems
• how to conduct experimental studies and write scientific papers

• how to contribute to the scientific community

Language of instruction and examination

Teaching methods