PhD Fellow in Multi-Hazard Early Warning System for Critical Underwater Infrastructure Protection

Om stillingen

The applicants will be assessed by an expert committee. The committee's mandate is to undertake an assessment of the applicants' qualifications based on the written material presented by the applicants, and the detailed description draw up for the position. A copy of the assessment report will be sent to all applicants.

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

This position requires a master's degree or equivalent in Information Technology, Computer Science, Electrical/Electronic Engineering, Automation, Cybernetics, Physics, Mathematics, Statistics, Nautical Science, Maritime Technology, or a similar field with some sea-going experience and stronger software development experience, and any other requirements. The Master's degree should be recognized as equivalent to a 2-year Norwegian Master's degree. The grade for the Master's degree should be at least a B.If you are near completion of your master's degree, you may still apply.

The position will contribute to guest lectures and research results for further development of the course portfolio of the program.

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. Working knowledge of Norwegian or a Scandinavian language is also beneficial.

It is preferable if the candidate has knowledge and experience in the following fields:

• Digital signal processing and digital filter design
• Frequency domain analysis and application development experience
• Maritime operational knowledge & experiences.
• Large Language Model-based application development.
• Deep Learning techniques, Data Engineering, and Semantic Technologies
• Open-source artificial intelligence, machine learning, statistical estimation methods, software tools, and big-data frameworks.
• Programming languages such as e.g. Python, C++, and LABVIEW.

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, professional experience and other experience of significance for the completion of the doctoral programme will be taken into consideration.

We will also emphasize motivation and personal suitability for the position. We are looking for candidates who:

• 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 of 120 ECTS, or an integrated master's degree of 300 ECTS.

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.