The position and project

A PhD position is available at the Department of Computer Science with the research group Arctic Green Computing. It is allocated to the project “Scalable machine learning for online forecast and control of ventilation resources in operation theaters – SmartVentilate”. The position is available for a period of 4 years.

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.

Large-scale analysis based on big data is crucial for scientific discovery and societal digitalization. The large-scale analysis has driven the development of scalable and adaptive machine learning methods to automatically process large amounts of data that have to be stored on many machines. Although scalable machine learning methods available today (e.g., deep learning) can provide high prediction accuracy, they provide little knowledge and insights into the resulting models. Moreover, they use rigid architectures which restrict their adaptability to the changing needs of learning.

This project will devise novel scalable machine learning methods and systems for which the use case is to pertain continuous and online control of ventilation for patients in operation theaters and intensive care units that are dependent partially or completely on assisted ventilation. Large hospitals such as UNN typically have individual ventilation units per patient as well as distributed supply of oxygen and other resources for ventilation units. This project will investigate and develop new machine learning methods that scale on modern extreme-scale computing systems, adapt themselves to the need of learning, and facilitate interpretation. The theoretical foundations of machine learning will be re-examined to develop new learning methods with improved interpretability and adaptability.

The position is for a period of four years. The nominal length of the PhD program is three years. The fourth year is distrubuted 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.

Qualifications

This position requires a Master’s degree or equivalent in Computer Science. A successful candidate should have a strong interest in at least one of the following topics: high-performance computing systems and fundamental machine learning. Since our research results are evaluated experimentally, good programming and system research skills are necessary.

Candidates in the final phase of their Master study may apply. A preliminary version of the dissertation can be included if the final version is not ready before the application deadline. Information about the delivery deadline for the dissertation must be given. A completed degree must be documented before an acceptable date for commencement.

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 candidates potential for research, motivation and personal suitability for the position.

The position requires admission to the Faculty’s PhD programme. 300 ECTS (European Credit Transfer System) is minimum requirement from cumulative of Bachelor degree and Master degree, including a master thesis of 30 ECTS or more. The applicant must have average grade of C or better on the Master’s degree. For non ECTS education; your application will only be considered for evaluation if the education meet the NOKUT guidelines. (Select the country and check if you fulfill the requirements). 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

Affiliation

The Department of Computer Science provides a strong international research environment with 18 tenured faculty members, 4 adjunct professors, 5 post doctors and researchers, 8 technical/ administrative staff members and about 28 PhD students. 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 data-center systems. The group current research topics include energy-efficient high-performance computing systems and large-scale data analytics. The group was a work-package leader in EU FP7 ICT project EXCESS on energy-efficient high-performance computing systems (2013 – 2016) and is PI and Co-PI of several national research projects funded by the Research Council of Norway (including FRIPRO Young Research Talents, Research Infrastructure and IKTPLUSS initiative). The group is a member of EU network of excellence HiPEAC on high performance and embedded architecture and compilation.

Contact

For further information about the position, please contact:

• Associate Professor Dilip K. Prasad dilip.prasad@uit.no
• Professor Phuong Hoai Ha phuong.hoai.ha@uit.no

For administrative questions, please contact the Department’s administration;phone: +47 7764 4036, email: administrasjon.ifi@nt.uit.no

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

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.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.

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.

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 abilit.

We 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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