PhD position within Robotics, Automation and Sensing
Deadline: 04.05.2026
Publisert
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The PhD position is for a period of 3 years, with a desired start date of 1st of September 2026.
The Robotics Group at NMBU works in two key areas: (1) mobile robotics for agricultural tasks and (2) manufacturing, particularly primary and secondary processing, prior to packaging. This position falls into the latter field, where the focus will be on projects related to industrial robots and their handling of food products. This represents a challenging area for robotics, where the raw materials have a high degree of variation (size, shape), as well as being easily damaged. Further, the perishable nature of many goods means that conventional approaches to robotics implementation are inadequate, either due to poor economic grounds or insufficient ability to perform tasks that are natural for human counterparts.
This position is not attached to a specific R&D project, although it is expected that the successful candidate would contribute to future projects, particularly due to the cross-cutting nature of the research challenges. This also gives the position some scope for topic definition within several key relevant areas. These are listed below, with a short justification for each.
Augmented 3D training sets using generative artificial intelligent approaches: Implementation of robotic systems based on AI approaches within production lines typically necessitates periods of training through the development life cycle. The extent of this training is governed by several factors, chief among them is the high degree of variation experienced in food raw materials. Those goods may have short shelf lives, and additionally may change colour, texture, and stiffness rapidly. Further, the lack of standardised 3D models for the wide variety of products makes offline learning challenging. As a result, the process of implementing and developing AI-based systems is high-risk, and can result in large losses or manual time required to rework products. Here therefore, a framework and approach to minimise such loss through the use of generative AI to augment training sets is proposed.
Multimodal sensing: The use of open-loop or simplified (e.g., single-sensor) closed-loop systems in food automation systems can have consequences for product quality and/or yield. The increased use of high-quality vision systems (including depth cameras and X-Ray) in robotics has extended capability significantly, but there are still often challenges of process accuracy or repeatability due to a lack of feedback close to the work object. Here therefore, the focus is on the use of several sensing modalities to close this gap, giving robots sufficient feedback to improve performance.
Collaborative operation: Robots offer the opportunity at production lines to augment existing workforces, particularly for tasks which are less complex, such as machine loading and unloading, or positioning of raw materials for specific processes. Batch production can make these processes irregular, thus configuration of a robot cell with fencing and so on is often cost and space prohibitive. Here therefore, the focus is on methods to enable robotic platforms to perform certain tasks in collaborative mode, such that they can be shifted from task to task, perhaps even working alongside human counterparts.
Main tasks
Define the topic definition within one of the defined key areas
Develop relevant use-cases related to the topic for the purposes of benchmarking
Develop necessary hardware and/or software capabilities relevant to the topic
Collect necessary data
Perform various experiments to document system development and benchmark against performance indicators or requirements
Write scientific papers for key journals and conferences in the field
Complete required courses
Competence
Required qualifications
A Master's degree or equivalent in a relevant Engineering discipline, or a related field. Foreign degrees must correspond with the admission criteria for the PhD program. Candidates submitting MSc thesis within 30. June 2026 may be considered.
In the Master’s degree, both the average grade point and the Master’s thesis, must be B or better according to the Norwegian educational system. Applicants who have recently graduated with excellent results may be given preference.
Demonstrable experience in at least one of the key areas (i.e., generative AI in 3D environments, sensor systems, collaborative robotics).
Personal suitability and motivation for the position.
Qualifications considered an advantage
Experience particularly with 3D vision systems.
Proficiency in the programming languages Python, C#, C and C++.
Experience working with industrial robots (e.g., ABB, Universal Robots, Kuka, Yaskawa).
Skills in academic writing (e.g., previous conference or journal articles)
Personal qualities
Be highly motivated for completing a PhD
Be open-minded and eager to learn
Be goal-oriented, accurate, analytical and structured
Have strong communication and cooperation skills
Remuneration and further information
Salary in position as PhD Research Fellow, position code 1017, 550 800 NOK. For exceptionally well-qualified candidates a higher salary may be considered.
By applying, the candidate confirms that information and documentation submitted via the job application may also be used by NMBU in a possible admission process to the PhD program. Admission to a PhD program is a prerequisite for appointment to the PhD position.
For professional and social interaction, the candidate is expected to be physically present at NMBU on a daily basis.
Send application electronically via the link "Apply for this job" at the top of this page. This will route you to the University's Web Recruitment System, where you will need to register an account (if you have not already) and log in before completing the online application form.
Application Deadline: 04.05.2026
Your CV must be entered in Jobbnorge's CV form and not just included as an attachment. This is in order to be able to comply with the regulations to the Public Administration Act §15.
Applicants invited for an interview are expected to present original diplomas and certificates.
Interviews with the best qualified candidates will be arranged. Applicants invited for an interview are expected to present original diplomas and certificates.
The following documents must be attached to the application:
Motivation letter
CV
Certified copies of relevant academic diplomas and certificates. (i.e. Diploma, transcript. Diploma supplement for both bachelor and master). Diplomas, transcripts and diploma supplements that are not in Norwegian or English must be uploaded in the original language. An English translation of these documents must also be attached.
Applicants from universities outside Norway are kindly requested to send a diploma supplement, or a similar document, which describes in detail the study program and grading system.
Additional relevant documentation of professional knowledge (for example, list of scientific works). If it is difficult to judge the applicant’s contribution for publications with multiple authors, a short description of the applicant’s contribution must be included.
About The Faculty of Science and Technology
The Faculty of Science and Technology (REALTEK) develops research-based knowledge and educates civil engineers and lecturers needed to reach the UN's sustainability goals. We have approximately 150 employees, 70 PhD students and soon 1500 students. The education and research at REALTEK cover a broad spectrum of disciplines.
This includes data science, mechanics and process engineering, robotics, construction and architecture, industrial economics, environmental physics and renewable energy, geomatics, water and environmental engineering, applied mathematics as well as secondary school teacher education in natural sciences and use of natural resources such as in agriculture, forestry and aquaculture. The workplace is in Ås, 30 km from Oslo.
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