UiT Norges arktiske universitet

Fakultet for naturvitenskap og teknologi - Institutt for fysikk og teknologi

PhD Candidate in satellite remote sensing for ecological studies

UiT The Arctic University of Norway has a vacant PhD position for applicants who wish to obtain the degree of Philosophiae Doctor (PhD). The appointment is for a period of four years. The position is attached to the Machine Learning Group at the Department of Physics and Technology, Faculty of Science and Technology.

The position is one of four PhD positions announced as part of the multidisciplinary project “Methodological Advancement of Climate-ecological Observatory for Arctic Tundra (COAT Tools)” hosted by UiT The Arctic University of Norway. The other three positions are in the areas of computer science, statistics and educational research. The objective of the four projects is to develop sensor system technology, data analysis methods and research-based education related to observation of climate effects on ecosystems on the Arctic tundra.

The PhD position is for a fixed term, with the objective of completion of 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 PhD Candidate shall participate in the faculty’s organized research training, and the PhD project shall be completed during the period of employment. Information about the application process for admission to the PhD programme, application form and regulations for the degree of Philosophiae Doctor (PhD) is available at our website.

Further information about the position and project details is available by contacting:

Associate Professor Stian Normann Anfinsen, Department of Physics and Technology, UiT, by email stian.normann.anfinsen@uit.no or telephone +47 776 45173

Senior Research Scientist Jane Uhd Jepsen, Norwegian Institute for Nature Research, by email jane.jepsen@nina.no or telephone +47 417 65419.

The position’s affiliation

The Department of Physics and Technology (DPT) consists of five research groups: (1) Earth Observation, (2) Energy and Climate, (3) Machine Learning, (4) Space Physics, and (5) Ultrasound, Microwaves and Optics. The department provides education on the Bachelor, Master, and PhD levels, and comprises 18 permanent scientific positions and a technical/administrative staff of 10 persons. The department conducts research and education at a high international level.

The position is affiliated with the Machine Learning Group at DPT. The Machine Learning Group currently consists of two faculty members, one postdoctoral research fellow, and seven PhD students. The group focuses on methodology development within machine learning and statistical pattern recognition. The application areas include earth observation and health analytics, and two of the PhD projects are conducted in collaboration with companies under industrial PhD grants from the Research Council of Norway. The Machine Learning Group has strong collaboration with research groups abroad, as well as the Department of Mathematics and Statistics at UiT. The candidate can expect close collaboration with members of the COAT project (www.coat.no) and the BIRCHMOTH group (www.birchmoth.no) at NINA and UiT, and interaction with the other PhD students employed under the COAT Tools projects.

The project “Methodological Advancement of Climate-ecological Observatory for Arctic Tundra (COAT Tools)”

The circumpolar arctic tundra is the Earth’s terrestrial biome where the rate of climate change is highest. The extent of warming projected from global circulation models is so extreme that tundra ecosystems will likely transform into novel states within a few decades – potentially leading to loss of important functions (including those providing services to humanity) and biodiversity. However, climate-ecological models cannot reliably predict such transitions and their consequences without the input of substantially increased streams of integrated measurements (observations) of climate and ecosystem state variables. Because such data streams from climate-ecological observatories are deficient in the terrestrial Arctic, UiT and partners from the Fram – High North Research Centre for Climate and the Environment have developed the Climate-ecological Observatory for Arctic Tundra (COAT). This project has been awarded status as a “National Research Infrastructure” by the Research Council of Norway and the subproject “COAT Infrastructure” has been granted funding to implement logistics and instrumentation in Norwegian terrestrial Arctic.

The COAT science plan outlines a systematic scheme for continuously generating and disseminating new knowledge about climate change impacts on arctic tundra ecosystems. According to this plan, predicted climate impact paths derived from food web models define the scope of observation systems and data processing and the statistical models (i.e. data analyses and prediction tools) that will generate new knowledge about the state of the system. This new knowledge shall facilitate updating of the food web models as well as an education interface with primary and secondary schools. This education interface involves two components; knowledge dissemination (research-based education) and citizen Science (input to the observation system).

The COAT Tools project represents an ambition to make a methodological advancement of COAT - beyond its published science plan and the current infrastructure project. This will be achieved by involving an interdisciplinary team of scientists from three faculties and five departments at UiT. In addition to the COAT ecologists from the Department of Arctic and Marine Biology and the Norwegian Institute for Nature Research (NINA), COAT Tools will include scientists from Department of Physics and Technology, Department of Computer Science, Department of Mathematics and Statistics, and Department of Education. COAT Tools comprises four work packages that are separate PhD projects with main supervisors from each of the latter departments.

The position’s field of research

The position’s field of research is remote sensing of vegetation parameters in the forest-tundra ecotone. Optical satellite images have previously been exploited in COAT for mapping of moth outbreaks and resulting damage in subarctic birch forest. This project aims to extend COAT’s use of remote sensing data for retrieval of vegetation parameters that hold key information for ecosystem modelling. This will be done by combining information from optical sensors and synthetic aperture radar instruments. Specific topics to be studied are: (i) the structure of regenerating forest in areas affected by moth outbreaks, but subject to contrasting reindeer herding regimes; and (ii) spatio-temporal mapping of Salix shrub, whose distribution is an important indicator for climate change, and which constitutes key habitat for tundra birds, in particular ptarmigan. Both topics will be studied by use of high-resolution and multichannel radar and optical images that will be compared with in situ measurements and observation to determine the reliability and limitations of remote sensing based inference. Dense time series of low-cost radar and optical images will be investigated for their contribution of phenological information and with the aim of upscaling the retrieval process to wide coverage and long term monitoring.

Qualification requirements

We are looking for a candidate with strong analytical skills, experience with remote sensing and analysis of geographical data, and a sound background in mathematics and/or statistics and quantitative analysis. Candidates with a range of backgrounds will be considered, including ecology, geography, biology, forestry, remote sensing, statistics, mathematics and electronic engineering. Documented knowledge of and experience with statistical analysis is required. Experience with one or more of the following is considered an advantage: programming (in Matlab, Python, R or similar languages), pattern recognition, image analysis, data processing, and use of software related to remote sensing, statistics and image processing. The candidate should have an interest in both theoretical and applicative aspects of data analysis, and must be prepared to develop algorithms and software. Experience with remote sensing of vegetation and a basic knowledge of biology and ecology is an advantage, but not a requirement. It is expected that the candidate will participate in field campaigns to collect in situ validation data on forest and vegetation parameters.

Due to the wide range of educational backgrounds that will be considered for the position, the evaluation will assess the candidate’s combined expertise versus possible ways of adjusting the project to varying levels of ability in quantitative analysis and computer programming, and understanding of ecology and vegetation.

Emphasis shall also be attached to personal suitability.

The successful applicant must fulfil the requirements for admission to the faculty’s PhD programme. In addition, he/she shall be able to document proficiency in English equivalent to Norwegian Higher Education Entrance Qualification, refer to the website about PhD regulations at UiT.

Working conditions

The normal period of employment is four years. The nominal length of the PhD programme is three years. The fourth year is used for teaching or other duties for the university, cf. Guidelines for the research fellow’s duties. The teaching duties are spread about equally over the four years. The position will especially be assigned teaching duties at the Department of Physics and Technology.

A shorter period of appointment may be decided when the research fellow has already completed parts of his/her research training programme or when the appointment is based on a previous qualifying position (PhD Candidate, 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 Candidate 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.


An expert committee will assess the applicants. During this assessment process, emphasis will be attached to the applicant’s potential for research as shown by:

  • Master’s thesis or equivalent
  • any other academic works, and
  • state of purpose (cover letter) highlighting the candidate’s background and its relevance to the job announced
  • skills in scientific writing in English

In addition, consideration may be given to work experience or other activities of significance for the implementation of the PhD studies, and to any teaching qualifications. This includes experience with popularization, teaching or other types of dissemination of technical and scientific work. Information and material to be considered during the assessment must be submitted by the stipulated deadline.

The applicants who are assessed as the best qualified will be called to an interview. The interview shall among other things aim to clarify the applicant’s personal suitability and motivation for the position.


The application must be submitted electronically via the application form available on www.jobbnorge.no.

In addition, by the application deadline, the application must contain:

  • 1-2 page letter of application (cover letter) highlighting the candidate’s background and its relevance to the announced job
  • CV (containing a complete overview of education, supervised professional training and professional work)Certified* copies of:
    • diploma and transcript from your Bachelor’s degree or equivalent
    • diploma and transcript from your Master’s degree or equivalent
    • diploma supplement for completed degrees
    • documentation of English language proficiency
    • references
  • List of works and description of these (see below)
  • The list of works shall contain the following information:
    • author(s), the work’s title
    • for articles: the journal’s name and volume, the first and last page of the article, year of publication
    • for publications: publisher, printer, year of publication, number of pages

The works (published or unpublished) which the applicant wishes to be taken into consideration during the assessment process must be submitted.

* All photocopies of certificates, diplomas, transcript and reference letter must be stamped and certified by the photocopying or a public office.

All documentation that is to be evaluated must be certified and translated into English or a Scandinavian language.

Information and material to be considered during the assessment must be submitted by the specified deadline.

Applicants invited for an interview will be asked to bring original certificates and diplomas.

General information

Applicants shall also refer to the Supplementary regulations for appointment to postdoktor (Postdoctoral Research Fellow), stipendiat (PhD Candidate) and vitenskapelig assistent (Research Assistant) positions at UiT and to the Regulations concerning terms and conditions of employment for the posts of postdoktor (Postdoctoral Research Rellow), stipendiat (PhD Candidate), vitenskapelig assistent (Research Assistant) and spesialistkandidat (Resident), refer to the website with information for applicants for positions at UiT.

Questions concerning the organisation of the working environment, such as the physical state of the place of employment, health service, possibility for flexible working hours, part time, etc. as well as questions about the PhD programme may be directed to the telephone reference in this announcement.

UiT has HR policy objectives that emphasize diversity, and encourages all qualified applicants to apply regardless of their gender, functional ability and national or ethnic background.

UiT is an IW (Inclusive Workplace) enterprise, and will emphasize making the necessary adaptations to the working conditions for employees with reduced functional ability.

Personal data given in an application or CV will be processed in accordance with the Act relating to the processing of personal data (the Personal Data Act). In accordance with Section 25 subsection 2 of the Freedom of Information Act, the applicant may request not to be registered on the public list of applicants. However, the University may nevertheless decide that the name of the applicant will be made public. The applicant will receive advance notification in the event of such publication.

We look forward to receiving your application!



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  • Søknadsfrist
    7. april 2017
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    UiT Norges arktiske universitet
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