A PhD position is available at the Department of Mathematics and Statistics, Faculty of Science and Technology and focuses on developing new methods for incorporating heterogeneous data into deep learning algorithms.  

The project is an extension to the ongoing RCN 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 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 workplace is at UiT in Tromsø. The position is available for commencement from 01.01.24. You must be able to start in the position in Tromsø within a reasonable time, and not later than 6 months after receiving the offer.

The project will have close cooperation with Forsvarets Forskningsinstitutt (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 the Department of Geosciences at UiT. The candidate will also interact with researchers in Visual intelligence (a center for research-driven innovation focusing on novel deep learning-based solutions for complex image analysis) and Integreat (a center of excellence focusing on combining statistics, logic, and machine learning to contribute to more knowledgedriven machine learning).

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 is realized for applications relying on complex visual data, e.g., within civilian seafloor monitoring and defense applications of underwater robotics.

Most traditional deep learning models are designed to work with unimodal data, such as images, text, or audio. However, in many applications the available data is often heterogeneous and consist of several different modalities, motivating a need for research into how to best utilize this increased diversity of information. Heterogeneous data can provide complementary information which can be used to improve the prediction accuracy and robustness of deep learning models. Moreover, interactions between data from diverse sources can reveal complex patterns not available in individual sources of data. On the other hand, heterogeneous data also poses new challenges. Harmonizing between diverse data types, formats and structures requires specialized techniques, and combining different modalities must manage semantic differences between the various sources of data as disparities in meaning and representation across different types of data can complicate the process of effectively integrating information from diverse sources.

Utilizing heterogeneous data is highly relevant to ocean surveying as it enables the integration of data from sources such as digital 2D images, acoustic images, video sequences as well as auxiliary data such as contextual descriptions, location, and various seabed geophysical measures. This allows for a more comprehensive and accurate understanding of marine environments and ecosystems.

This project will develop novel directions within multimodal deep learning which utilizes heterogeneous data to improve deep learning models for environmental monitoring of the seabed.

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, which requires 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.

For further information about the position, please contact Professor Fred Godtliebsen: 

• email: fred.godtliebsen@uit.no

This position requires a Norwegian 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’s degree.

Applicants must document fluency of in English and be able to work in an international environment. Nordic applicants can document their English capabilities by attaching their high school diploma. International experience is an advantage.

The suitable candidate must have:

• Experience in signal and image processing.
• A solid background in machine learning, statistics, mathematics, and linear algebra.
• Excellent programming skills.

Experience with deep learning and statistics (through courses, research projects, or similar), including hands-on experience with software tools such as PyTorch and TensorFlow, 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 an asset.

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. We are looking for candidates who:

• Independence and self-motivation
• Creativity and ability to think outside the box
• Excellent work ethics and commitment to the job
• Have good collaboration skills
• Have good communication and interaction with colleagues and students
• Wants to contribute to a good working environment

As many people 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:  

• A bachelor's degree of 180 ECTS and a master's degree, or an integrated master's degree.

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.

• Involvement in an interesting project within a highly innovative centre environment
• Opportunities to travel and meet other leading scientists within the field
• Independence in work
• A fantastic work environment with nice colleagues
• Good career opportunities  
• Flexible working hours and a state collective pay agreement  
• Pension scheme through the state pension fund 
• PhD Fellows are normally given a salary of 532 200 NOK/year with a 3% yearly increase
• a cosy hometown of Tromsø surrounded by the stunning landscape of Northern Scandinavia

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: 

• Application and motivation letter (max 1 page)
• CV (max 2 pages)
• 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
• Three references, preferably including the master thesis supervisor
• Master’s thesis, and up to 4 other academic works
• Description of your academic production (any publications)

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. You must document completion of your degree before commencement in the position.

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 nondisclosure. You will receive advance notification in the event of such publication, if you have requested non-disclosure.

UiT The Arctic University of Norway is a multi-campus comprehensive university at the international forefront. Our vision is to be a driving force for developing the High North. The Northern Sami notion eallju, which means eagerness to work, sets the tone for this motive power at UiT. Along with students, staff and the wider community, we aim to utilise our location in Northern Norway and Sápmi, our broad and diverse research and study portfolio and interdisciplinary advantage to shape the future.

Our social mission is to provide research-based education of high quality, perform artistic development and carry out research of the highest international quality standards in the entire range from basic to applied. We will convey knowledge about disciplines and contribute to innovation. Our social mission unites UiT across various studies, research fields and large geographical distances. This demands good cooperation with trade and industry and civil society as well as with international partners. We will strengthen knowledge-based and sustainable development at a regional, national and international level.

Academic freedom and scientific and ethical principles form the basis for all UiT’s activities. Participation, co-determination, transparency and good processes will provide the decision-making basis we need to make wise and far-sighted priorities. Our students and staff will have the opportunity to develop their abilities and potential. Founded on academic integrity, we will be courageous, committed and generous in close contact with disciplines, people and contemporary developments.

We will demonstrate adaptability and seek good and purposeful utilisation of resources, so we are ready to meet the expectations and opportunities of the future. We will strengthen the quality and impact of our disciplines and core tasks through the following three strategic priority areas.