The Department of Computer Science at the Norwegian University of Science and Technology (NTNU) offers a postdoc fellowship in Edge Intelligence. The fellowship addresses fundamental challenges of edge computing and artificial intelligence, which in theory supports various applications, such as Extended Reality, the Internet of Things, and smart sensing. The selected fellow will be exposed to cutting-edge research enabling edge intelligence and publishing research results in top journals. The fellow will have 29% of mandatory teaching services. In addition, the postdoc fellow will have the opportunity to participate in international activities, such as conference attendance, International collaborations, and mobility, and contribute to organizing conferences/workshops.
- Applicant must possess a Norwegian Ph.D. degree or corresponding foreign Ph.D. degree recognized as equivalent to a Norwegian Doctoral degree in computer science, data science, communications, information systems, etc.
- Applicants must be familiar with distributed computing, mobile computing, data science, or networking.
- Candidates submitting and defending their dissertations are encouraged to apply, but the doctoral degree must be completed before taking up the position.
- Applicant must demonstrate a good research publication record in the relevant areas.
- Applicants with Software engineering and system development skills would have an advantage.
- Applicant must be willing to conduct research independently and in association with others.
- Applicant must possess excellent English language skills (written and oral).
Apply online and have the following:
- A cover letter describing personal motivation and relevance
- Resume including information about prior education, work experience, academic merits, and any scientific publications.
- A draft research proposal (maximum three pages) presents their ideas for the postdoc and how they can be applied.
- Transcripts and diplomas of the degrees earned till now.
- A copy of the Ph.D. thesis.
- Published or unpublished academic works (up to 3 papers).
- Three references with names and contact information.
Deadline: 31st January 2023