Ledig stilling ved UiT Norges arktiske universitet
PhD fellow in Computer Science – Artificial Intelligence based lifestyle modelling
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.
Faculty of Sciences and Technology
A PhD position in Computer Science is available at the Department of Computer Science and allocated to the project: LifestyleModelling - Modelling physical activity lifestyle in free living individuals using new deep learning architectures.
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 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. Information about the application process for admission to the PhD programme, application form and regulations for the degree of Philosophiae Doctor (PhD) are available at here.
The Department of Computer Science provides an active 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.
Physical activity is an important component of healthy living. In population studies it is investigated both as a predictor of health or disease, or as outcome of physical, mental and social conditions. Considerable research has been done recently on physical activity measured by sensor devices, especially on estimation of energy expenditure, or classification of types of activity. However, most of the research uses comparably simple variables, for instance the number of minutes in moderate to vigorous physical activity per hour or day. The availability of high-resolution temporal data, for example acceleration data of participants sampled at e.g. 100 Hz over one week, allows for new approaches in the investigation of physical activity utilizing the complex information hidden in these time series.
This PhD project will create a physical activity lifestyle model of free living individuals using deep learning architectures. The project is based on data from the Tromsø Study. This cohort study has in its recent follow-up examination acquired a large number of one-week accelerometer measurements, and for a part of these participants 24-hours ECGs, in addition. There is a large number of health variables available for the study participants. A subset of these variables will be used to investigate to what extent the physical activity lifestyle can predict health outcome. The project will explore new deep learning architectures to create a physical activity lifestyle model that is capable of learning over time.
For further information about the position, contact:
- Professor Alexander Horsch, [email protected] or
- Associate Professor Dilip K. Prasad, [email protected]
For administrative questions, please contact the Department’s administration, phone +47 77644036, email: [email protected]
- A good academic environment with dedicated colleagues
- Flexible working hours and a state collective pay agreement
- Pension scheme through the state pension fund
More practical information for working and living in Norway can be found here: Welcome to UiT!
This position requires a Master’s degree or equivalent in Computer Science, or Mathematics and Computing.
In addition, the candidate must have:
- experience of working with computer vision and deep learning toolkits on at least one of the platforms Python, C/C++, MATLAB, Keras, PyTorch, or Tensor Flow;
- demonstrated programming proficiency in at least two platforms like e.g. Python, C/C++, MATLAB, OpenCV;
- postgraduate coursework or master thesis strongly related to at least four of the topics:- Machine learning/deep learning- Artificial Intelligence- Optimization theory/ convex optimization/computational optimization- Linear algebra; Statistics/statistical machine learning- Computational modelling of differential and integral equations- Data science- GPU programming- Neural networks- Robotics or sensor intelligence- Internet of things programming and big data
A successful candidate will have strong interest in at least one of the topics:
- fundamental machine learning
- neural network architecture
- artificial intelligence;
- human activity based data analytics.
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.
During the assessment, emphasis will be put on your potential for research in the described field, 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.
The applicant should in addition be able to document proficiency in English equivalent to Norwegian Higher Education Entrance Qualification.
Further information about requirements and the PhD programme is available in the Regulations PhD Faculty of Sciences and Technology.
The application must be submitted electronically via www.jobbnorge.no and shall include:
- Cover letter explaining your motivation and research interests
- Diplomas, diploma supplements and transcripts (all degrees)
- Documentation on English proficiency.
- 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.
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.
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.