The position

A PhD position is available at the Department of Computer Science with the Arctic Green Computing (AGC) research group and the Arctic Centre for Sustainable Energy (ARC). The position is attached to the research project Energy-efficient machine learning (GreenML).

UiT The Arctic University of Norway

UiT is a multi-campus research university in Norway and the northernmost university of the world. Our central location in the High North, our broad and diverse research and study portfolio, and our interdisciplinary qualities make us uniquely suited to meet the challenges of the future. At UiT you can explore global issues from a close-up perspective.

Credibility, academic freedom, closeness, creativity and commitment shall be hallmarks of the relationship between our employees, between our employees and our students and between UiT and our partners.

The position is for a period of four years. The nominal length of the PhD program is three years. The fourth year is distributed as 25 % each year, and will consist of teaching and other duties. The objective of the position is to complete research training to the level of a doctoral degree. Admission to a PhD programme is a prerequisite for employment, and the programme period starts on commencement of the position.

The Department of Computer Science provides an active international research environment with 18 tenured faculty members, 4 adjunct professors, 6 post-doctors and researchers, 8 technical/administrative staff members and about 28 PhD students from different countries. The goal of the Department is to advance the research and teaching of Computer Science as a discipline, to demonstrate leadership within our areas of interest, and to contribute to society through our education, research, and dissemination.

The Arctic Green Computing (AGC) research group aims at addressing energy efficiency, system complexity, and dependability across mobile, embedded, and datacenter systems. The group’s current research interests include (energy-) efficient, high-performance computing (Green HPC) and scalable, efficient artificial intelligence (Green AI). The group was a work-package leader in EU FP7 ICT project EXCESS on energy-efficient computing systems (2013-2016) and is PI and Co-PI of several national research projects funded by the Research Council of Norway (NFR) through prestigious funding programs such as FRIPRO Young Research Talents, Research Infrastructure and IKTPLUSS ICT Initiative. The group is a member of EU network of excellence HiPEAC and has international collaboration with prestigious institutions in EU and USA (e.g., Lawrence Berkeley National Laboratory, University of California - Berkeley).

In 2016, UiT established the Arctic Center for Sustainable Energy (ARC), an interdisciplinary center focusing on Arctic challenges and conditions within renewable energy and greenhouse gas management. The center combines expertise in computer science, physics, humanities, chemistry, social sciences, applied mathematics, marine biology, and electrical engineering. The initiative will permeate the university in its entirety and will strengthen existing research activities at UiT within the scope of the center.

The position's field of research

The exceptional progress of machine learning on a wide range of applications has been achieved by computationally-intensive deep learning models. The training cost of deep learning models has increased 300,000 times in 6 years (or 23 times in 18 months), far exceeding Moore’s law. Scientists have reported that the estimated CO¬2 emissions from training one big model is five times the CO2 emissions from a car, including fuel, for its lifetime. This trend is expensive and unsustainable, which has been driven by the intense focus on accuracy rather than efficiency.

This project aims at investigating and improving the energy- and resource-efficiency of machine learning models. The project will investigate efficiency metrics on which more efficient approaches can be developed. The research challenges include balancing accuracy and efficiency and developing models that are efficient yet still accurate. The project will investigate neural architecture search approaches for designing efficient, accurate models while considering the platform and application constraints (e.g., energy, resources, latency, real-time).

Smart power systems will be an application domain in this project.

Contact

Further information about the position is available by contacting Professor Phuong H. Ha:

• phone +47 77 64 40 32
• email: phuong.hoai.ha@uit.no

For administrative questions, please contact the Department’s administration;

• phone +47 77 64 40 36,
• email: administrasjon.ifi@nt.uit.no

Qualifications

This position requires a Master’s degree or equivalent in Computer Science. A successful candidate should have a keen interest in at least one of the following topics: machine learning and energy-/resource-efficient computing. Experience of real-time monitoring systems and low-power AI systems (e.g., Intel/Movidius NCS) is a plus. Since our research results are experimentally evaluated, excellent programming skills in C/C++ are necessary.

Qualification with a Master’s degree is required before commencement in the position. If you are near completion of your Master’s degree, you may still apply and submit a draft version of the thesis and a statement from your supervisor or institution indicating when the degree will be obtained. You must document completion of your degree before commencement in the position.

The applicant should in addition be able to document proficiency in English equivalent to Norwegian Higher Education Entrance Qualification, available here.

Emphasis will be put on the candidate’s potential for research, motivation and personal suitability for the position.

Admission to the PhD programme

The position requires admission to the Faculty’s PhD programme. Admission requires that the applicant has at least 5 years of higher education, equivalent to 300 ECTS. The applicant must have a Master’s thesis evaluated equivalent to 30 ECTS or more. The applicant must have average grade of C or better on the Master’s degree.

Applicants with a foreign education will be evaluated on whether the educational background is equivalent to Norwegian higher education, following national guidelines. Applicants from some countries will have to document additional higher education in order to fulfill the requirements.

Further information about requirements and the PhD programme is available here: Regulations PhD Faculty of Sciences and Technology

Application

Your application must include:

• Cover letter explaining your motivation and research interests
• CV
• Diplomas, diploma supplements and transcripts (all degrees)
• Documentation of English proficiency This website states how English proficiency must be documented.
• Written references
• Contact information to 1-3 references
• Master thesis, and any other academic works

The documentation has to be in English or a Scandinavian language. We only accept applications sent via www.jobbnorge.no.

General information

Remuneration for the position of PhD Fellow is in accordance with the State salary scale code 1017. A compulsory contribution of 2 % to the Norwegian Public Service Pension Fund will be deducted.

The appointment is made in accordance with State regulations and guidelines at UiT. At our website, you will find more information for applicants.

As many as possible should have the opportunity to undertake organized research training. If you already hold a PhD or have equivalent competence, we will not appoint you to this position.You have to be qualified for and participate in the PhD programme. A shorter period of appointment may be decided when the PhD Fellow has already completed parts of their research training programme or when the appointment is based on a previous qualifying position PhD Fellow, research assistant, or the like in such a way that the total time used for research training amounts to three years.

More practical information for working and living in Norway can be found here: http://uit.no/mobility

A good work environment is characterized by diversity. We encourage qualified candidates to apply, regardless of their gender, functional capacity or cultural background. UiT will emphasize making the necessary adaptations to the working conditions for employees with reduced functional abilitWe process personal data given in an application or CV in accordance with the Personal Data Act (Offentleglova). According to Offentleglova information about the applicant may be included in the public applicant list, also in cases where the applicant has requested non-disclosure. You will receive advance notification in the event of such publication, if you have requested non-disclosure.

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