LEDIG STILLING VED UIT NORGES ARKTISKE UNIVERSITET
PhD Fellow in Computer Science (Artificial Intelligence)
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 Science and Technology
The position is for a period of four years. The nominal length of the PhD programme 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 the 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 26 tenured faculty members, 11 adjunct professors, 5 post doctors and researchers, 7 technical/ administrative staff members and over 30 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 workplace is at UiT in Tromsø. You must be able to start in the position in Tromsø within a reasonable time after receiving the offer.
The position’s field of research
The position is allocated to the project Nanoscale artificial intelligence in microscopy and nanoscopy for life sciences (NanoAI), a Research Council of Norway funded project during 2021-2025. Partners from the Department of Physics & Technology and the Department of Clinical Medicine collaborate to provide raw microscopy data of biological systems, for which the AI solutions will be designed within this project.
The announced PhD position relates to developing computer vision and machine learning models, including interpretable and scalable artificial intelligence, for 3D microscopy (labeled and label-free) image and video data of biological samples such as heart cells and engineered heart tissues and performing AI-based analytics on such data. Interpreting processes inside living systems and label-free images/videos of cells and tissues is a daunting task.
The candidate will work on the problems: Images of biological samples appear as gray scale images devoid of color, texture, and edges. Therefore, they lack features conventionally used in deep models for identification of individual structures. New suitably designed and trained intelligence models have to be developed. If conventional AI approaches such as deep learning and generative adversarial networks are used, large training dataset with correlated image sets of labeled and label-free images are needed, which is a significant challenge. There is a need of new out-of-box AI solutions that derive and improve intelligence, as new data becomes available. 2D and 3D video segmentation, tracking, morphology analysis, graph-based artificial intelligence, time-series analysis, spatio-temporal pattern recognition, etc. will be undertaken. In addition, new AI solutions will be developed and adapted for biological microscopy data analysis problem.
Related research papers:
1. A.A. Sekh, I-S. Opstad, A.B. Birgisdottir, T. Myrmel, B.S. Ahluwalia, K. Agarwal, and D. K. Prasad, “Learning nanoscale motion patterns of vesicles in living cells,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Washington, USA, 14-19 June, 2020.
2. A.A. Sekh, I-S. Opstad, G. Godtliebsen, A.B. Birgisdottir, B.S. Ahluwalia, K. Agarwal, and D. K. Prasad, “Physics based machine learning for sub-cellular segmentation in living cells,” Nature Machine Intelligence, 2021.
Further information about the position is available by contacting:
- Associate Professor Dilip K. Prasad: [email protected]
For administrative questions, please contact Head of administration Svein Tore Jensen: phone +47 77644036, [email protected].
This position requires a Master’s degree or equivalent in Computer Science, Mathematics & Computing, or Engineering. Candidates in the final phase of their Master study may apply.
The other mandatory requirements are:
- Experience of working with computer vision and deep learning toolkits on at least one of the following platforms – Python, C/C++, MATLAB, Keras, PyTorch, Tensor Flow
- Demonstration of programming proficiency in at least 2 of the following platforms: Python, C/C++, MATLAB, OpenCV, etc.
- Postgraduate coursework or master thesis strongly related to at least 4 of the following topics:
- Machine learning/deep learning
- Computer vision
- Neural Networks
- Optimization theory/ convex optimization/computational optimization
- Linear algebra
- Statistics/statistical machine learning
- Computational modelling of differential and integral equations
- Data science
- GPU programming
- Topologies and/or graph theory
A successful candidate should have a strong interest in at least one of the following topics: fundamental machine learning, neural network architecture, artificial intelligence, and interpretable learning. Since our research results are evaluated experimentally, good programming and system research skills are necessary.
Applicants must document fluency of in English and be able to work in an international environment. Working knowledge of Norwegian or a Scandinavian language is beneficial.
We will also emphasize motivation and personal suitability for the position.
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.
Admission to the PhD programme
For employment in the PhD position, you must be qualified for admission to the PhD programme at the Faculty of Science and Technology and participate in organized doctoral studies within the employment period.
Admission normally requires:
- A bachelor's degree of 180 ECTS and a master's degree of 120 ECTS, or an integrated master's degree of 300 ECTS.
- A master's thesis with a scope corresponding to at least 30 ECTS for a master's degree of 120 ECTS.
- A master's thesis with a scope corresponding to at least 20 ECTS for an integrated master's degree of 300 ECTS.
In order to gain admission to the programme, the applicant must have a grade point average of C or better for the master’s degree and for relevant subjects of the bachelor’s degree. A more detailed description of admission requirements can be found here.
Applicants with a foreign education will be subjected to an evaluation of whether the educational background is equal to Norwegian higher education, following national guidelines from NOKUT.
If you are employed in the position, you will be provisionally admitted to the PhD programme. Application for final admission must be submitted no later than two months after taking up the position.
Inclusion and diversity
UiT The Arctic University of Norway is working actively to promote equality, gender balance and diversity among employees and students, and to create an inclusive and safe working environment. We believe that inclusion and diversity are a strength and we want employees with different competencies, professional experience, life experience and perspectives.
If you have a disability, a gap in your CV or immigrant background, we encourage you to tick the box for this in your application. If there are qualified applicants, we invite at least one in each group for an interview. If you get the job, we will adapt the working conditions if you need it. Apart from selecting the right candidates, we will only use the information for anonymous statistics.
- Involvement in an interesting research project
- Good career opportunities
- 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.
Your application must include:
- Cover letter explaining your motivation and research interests
- Diploma for bachelor's and master's degree
- Transcript of grades/academic record for bachelor's and master's degree
- Explanation of the grading system for foreign education (Diploma Supplement if available)
- Documentation of English proficiency
- References with contact information
- Master’s thesis, and any other academic works
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. You must submit a preliminary version of the thesis and information about the delivery deadline for the thesis must be given. You must document completion of your degree before commencement in the position. You must still submit your transcripts for the master’s degree with your application.
All documentation to be considered must be in a Scandinavian language or English. Diplomas and transcripts must also be submitted in the original language, if not in English or Scandinavian. We only accept applications and documentation sent via Jobbnorge within the application deadline.
The appointment is made in accordance with State regulations and guidelines at UiT. At our website, you will find more information for applicants.
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.
We process personal data given in an application or CV in accordance with the Personal Data Act (Offentleglova). According to the Personal Data Act 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.