A PhD position in deep learning and statistics with focus on uncertainty estimation in classification is available at the Department of Mathematics and Statistics, Faculty of Science and Technology.

The position is financed by the RNC (Research Council of Norway) funded project “Fast uncertainty estimation in deep learning applied to object recognition in sonar images”. The focus of the position is to develop near real-time uncertainty estimation for deep learning algorithms and demonstrate how they can be useful both for civilian seafloor monitoring and defense applications of underwater robotics.

The project is an extension to the ongoing RNC funded project “Transforming ocean surveying by the power of DL and statistical methods” where academia and collaborating private sector partners (Multiconsult, Argeo) aim at improving and refining present AI classification methodology using novel combinations of statistical methods and Deep Learning (DL).

The position is for a period of three years. 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 workplace is at UiT in Tromsø, but the candidate will also spend longer periods at the Norwegian Defense Research Establishment (FFI) to collaborate with participating researchers.  The position is available for commencement. You must be able to start in the position in Tromsø within a reasonable time, within 6 months after receiving the offer.

The project will have close cooperation with FFI and could include technologies referred to in the foreign Ministry's export control regulations, and the candidate must be able to obtain a security clearance.

The successful candidates will work at the machine learning group at UiT and will formally be affiliated with the Department of Mathematics and Statistics and collaborate closely with researchers at Department of Geosciences at UiT. The candidate will also interact with researchers in Visual intelligence (a center for research-driven innovation) and Integreat (a center of excellence).

Deep learning has led to a range of new image-based technologies that are rapidly changing society. Despite these advances, it is still a long way before the potential of deep learning for visual intelligence is realized for applications relying on complex visual data, e.g., within civilian seafloor monitoring and defense applications of underwater robotics.

The popularity of modern deep learning applications is motivating a need for a better understanding of uncertainty measures in predictive modelling by neural networks. Many applications carry critical implications when data is misclassified. Predictive accuracy may not be a sufficient benchmark for such models – it is also necessary to develop measures of confidence in the predictions that are made, so that appropriate decisions can be made after taking the risk of misclassification into consideration.

Reliable uncertainty estimates in classification tasks are highly relevant to investigations performed by FFI with a main target application being to detect and clear hazardous mines/bombs within a given marine area. The project will:

The project will transfer knowledge and expertise to the defense sector through research and development carried out by all partners, including FFI.

The preceding techniques should be examined theoretically, but with an emphasis of practical application to data and use cases as dictated by the partnering organizations. For this reason, the research will concern a strong focus on practical implementations, requiring insight into:

Applied deep learning using popular deep learning libraries and frameworks, such as PyTorch, Tensorflow, or JAX

The use of probabilistic programming languages (Stan, Pyro, PyMC3, etc) for Bayesian and ensemble approaches to neural network implementation, and the intersection thereof with deep learning frameworks

Software and data engineering concepts for developing tenable, scalable implementations of deep learning training programs as well as data pipelines for online deployment of trained neural network models.

Contact

For further information about the position, please contact: 

This position requires a master’s degree in physics, mathematics/statistics, computer science, or similar, or a corresponding foreign master's degree recognized as equivalent to a Norwegian master degree. If you are near completion of your master’s degree, you may still apply.

The suitable candidate must have:

Experience with deep learning and statistics (through courses, research projects, or similar), including hands-on experience with software tools such as PyTorch, will be considered a strength.  

Knowledge of pattern recognition and big data processing, plus previous experience in applications related to seafloor monitoring and underwater robotics is considered as an asset.

Applicants must document fluency in English and be able to work in an international environment. International experience is an advantage. 

In the assessment, the emphasis is on the applicant's potential to complete a research education based on the master's thesis or equivalent, and any other scientific work. In addition, other experience of significance for the completion of the doctoral programme may be given consideration.

We will also emphasize motivation and personal suitability for the position. Other required qualification skills include:

As many poeple 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.

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:  

UiT normally accepts higher education from countries that are part of the Lisbon Recognition Convention.

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. 

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. 

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.

Norwegian health policy aims to ensure that everyone, irrespective of their personal finances and where they live, has access to good health and care services of equal standard. As an employee you will become member of the National Insurance Scheme which also include health care services.

More practical information about working and living in Norway can be found here: https://uit.no/staffmobility

Your application must include: 

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 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. If English proficiency is not documented in the application, it must be documented before starting in the position. 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. The engagement is to be made in accordance with the regulations in force concerning State Employees and Civil Servants, and the acts relating to Control of the Export of Strategic Goods, Services and Technology. Candidates who by assessment of the application and attachment are seen to conflict with the criteria in the latter law will be prohibited from recruitment to the announced position.

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. You will become a member of the Norwegian Public Service Pension Fund, which gives you many benefits in addition to a lifelong pension: You may be entitled to financial support if you become ill or disabled, your family may be entitled to financial support when you die, you become insured against occupational injury or occupational disease, and you can get good terms on a mortgage. Read more about your employee benefits at: spk.no.

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.

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. 

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