Admission requirements

Bachelor’s degree in engineering (180 credits), or other relevant technological degree on Bachelor level.

The bachelor’s degree must contain minimum 25 credits mathematics, 5 credits statistics and 7.5 credits in physics. Some of the courses in the bachelor program have a certain amount of statistics included and can be accepted.

Relevant field of study may be automation, process and gas technology, nautical science, mechanical, processing, safety, civil engineering. Other fields of study may be considered upon individual assessment.

Applicants must have a minimum grade average comparable to a Norwegian C (2.5) in the ECTS scale, see the UiT webpage for International admissions for more information on how the point average is calculated.

Applicants with education from non-Nordic countries must document English language proficiency. You will find more information on English language requirements on the UiT webpage for International admissions.

Application code:

• Nordic applicants: 4021
• EU / EEA / Switzerland applicants: 7105
• Non-EU/EAA applicants: 2037

Course content

Objectives of the course

Knowledge

The student ...

• has advanced knowledge of Sensing principles and system architectures, including acoustic, optical, inertial, radar, GNSS, environmental, and mechanical measurement systems.
• has thorough knowledge of measurement theory and uncertainty, sensor error models, noise sources, calibration methods, and uncertainty propagation in measurement chains.
• has knowledge of environmental and operational contexts affecting sensor performance.
• has thorough knowledge of data processing and sensor fusion, theoretical foundations of filtering, statistical estimation, and real-time data integration for systems.
• has thorough knowledge of system integration and standards, knowledge of communication protocols (e.g., NMEA, AIS, IEC 61162), digital twins, and data interoperability frameworks in industrial operations.
• has knowledge of emerging trends, including optical and photonic sensing, autonomous and distributed sensor networks, and AI-based measurement analytics for industrial applications.
• has knowledge of ethical, environmental, and societal dimensions of maritime sensing, reliability, data trustworthiness, and sustainability considerations in sensor system deployment.

Skills

The student ...

• can analyze and model the performance of individual sensors and their behavior under varying environmental and operational conditions.
• can quantify uncertainty and evaluate measurement quality using statistical and analytical tools.
• can design conceptual sensor integration architectures, including data acquisition, signal conditioning, and information fusion layers.
• can interpret and critically evaluate sensor data for applications such as navigation, situational awareness, machinery health monitoring, and environmental observation.
• can apply system theory to maritime cyber-physical systems, demonstrating the interdependence between sensing, control, and decision-making.
• can use analytical and computational tools (e.g., MATLAB/Python-based modeling) to simulate sensor performance and evaluate data processing algorithms.
• can communicate and document complex sensor system analyses and measurement designs to both technical and interdisciplinary audiences.
• can analyze existing theories, methods and interpretations in the field and work independently on practical and theoretical problems.

General Competence

The student ...

• can integrate multidisciplinary knowledge from engineering, instrumentation, and data science to assess and design industrial sensing systems.
• can critically reflect on the role of sensors and measurements in achieving safe, efficient, and environmentally responsible industrial operations.
• can collaborate in cross-disciplinary teams to address complex system-level challenges in industrial monitoring and control.
• can evaluate new technologies and research within industrial sensing and assess their potential impact on future industrial systems.
• can contribute to innovation and research in areas such as autonomous vessels, smart ports, or marine environmental monitoring, demonstrating scientific maturity and ethical awareness.
• can apply his/her knowledge and skills in new areas in order to carry out advanced assignments and projects.

Language of instruction and examination

Teaching methods