Reference number ORU 2.1.1-03949/2026
We are looking for a postdoctoral researcher in Computer Science for a fixed-term 2-year position at the School of Science and Technology.
Subject area
The subject area for this position is Computer Science, with a focus on machine learning and signal processing applied to proprioceptive sensing.
Background
We are looking for a postdoctoral researcher to tackle a hard and impactful sensing problem: developing machine learning approaches for turning the vibration signals of mining machines into precise, bucket-by-bucket estimates of how finely rock has been fragmented underground, where cameras and conventional sensors struggle.
Crushing and grinding of ore in the mining sector accounts for roughly 3% of the global total electricity use. Better fragmentation data enables energy and efficiency gains across the entire production chain.
The position is funded by an industrial research project in collaboration with Epiroc and Boliden, funded by the Swedish Energy Agency (Impact Innovation, Swedish Metals & Minerals). The two main goals of the project are to advance proof-of-concept results for proprioceptive sensing toward a validated technology and to extend the capabilities of today's state-of-the-art methods to extracting a richer set of material characteristics from piled materials.
The position is associated with the Robot Navigation and Perception Lab (https://rnp.aass.oru.se) which belongs to the Centre for Applied Autonomous Sensor Systems (AASS) research environment (https://www.oru.se/english/research/research-environments/ent/aass/) and Örebro University’s AI, Robotics, and Cybersecurity center (ARC) (https://www.oru.se/english/our-profile/arc-ai-robotics-and-cybersecurity-center/). We are an international team working with robotics and AI applied to real field conditions, and you will have access to real machines, real underground environments, and industrially relevant data, with a clear path from research to deployment.
Duties and responsibilities
The appointment as a postdoctoral researcher is intended to enable persons who have recently been awarded their doctoral degree to consolidate and develop primarily their research skills.
The position offers the opportunity to develop an independent research profile in machine learning and signal processing for proprioceptive sensing, within an active project on rock fragmentation estimation in mining. Building on proven results that estimate the average fragment size from machine vibration signals, the central research challenge is to recover the full distribution of fragment sizes — a substantially richer characterisation that would extend what proprioceptive sensing can deliver. The postdoctoral researcher will help shape how this problem is approached and develop and evaluate methods to address it.
There is further scope to investigate how fragmentation relates to material-handling efficiency, and how reliable estimation can run onboard equipment in operational settings, working with real data gathered together with industrial partners. The researcher will publish in peer-reviewed venues, present at scientific and industry conferences, and contribute to the wider research environment at AASS.
Qualifications
Those qualified for appointment as a postdoctoral researcher are applicants who at the time of appointment hold a doctoral degree or have a degree from abroad deemed to correspond to a doctoral degree. Applicants with doctoral degrees that have been awarded no more than 3 years prior to the application deadline are to be considered first. The applicant may not previously have held a position as a postdoctoral researcher for more than one year in the same subject and at the same higher education institution. The position is for a term of at least 2 years initially but may be extended for a further 1 year. If the person during the period of employment has been on parental leave, the period of employment is to be extended by the corresponding number of days. The period of employment may also be extended in the event of absence due to illness.
Assessment criteria
A basis for the assessment is the applicant’s ability to disseminate information and communicate; collaborate and engage with the wider community; and facilitate utilisation of the university’s research. In addition, the applicant’s suitability for the position will be assessed. Suitability refers to the applicant demonstrating the personal qualities required to successfully perform the duties and responsibilities at hand, the ability to cooperate with other members of staff, and the ability to contribute to the development of the operations.
Particular attention is to be paid to the applicant’s prospects of contributing to the future development of both research and education. Importance is also attached to a demonstrated ability and ambition to embark on a career within academia.
Required: Doctoral degree in a quantitative discipline — statistics, applied mathematics, machine learning, signal processing, robotics, or a closely related field.
Required: Demonstrated experience in statistical modelling and/or machine learning for regression or distribution estimation, and in time-series or signal processing; proficiency in Python.
Advantage: Hands-on field or experimental experience acquiring and analysing real-world sensor data (e.g. IMU/accelerometer, vibration), and working with mobile/heavy equipment or in industrial settings; experience with edge/embedded deployment; ROS2.
Desirable: Domain familiarity with mining, fragmentation, geomechanics, or comminution; a valid driving licence and willingness to conduct site visits in harsh underground conditions.
Language: Working proficiency in English required. It is not necessary to be familiar with the Swedish language.
Personal qualities of particular importance: Ability to drive an independent research line; comfort moving between algorithm development and hands-on fieldwork; rigour and reliability in data handling; and strong collaboration across academic and industrial partners.
Information
This is a full-time position for two years. At Örebro University, salary depends on the successful candidate’s qualifications and experience.
For more information about the position, contact project leader Unal Artan (email: [email protected]) or head of unit Martin Magnusson (email: [email protected]).
At Örebro University, we expect each member of staff to be open to development and change; take responsibility for their work and performance; demonstrate a keen interest in collaboration and contribute to development; as well as to show respect for others by adopting a constructive and professional approach.
Örebro University actively pursues equal opportunities and gender equality as well as a work environment characterised by openness, trust and respect. We value the qualities that diversity adds to our operations.
Application
The application is made online. Click the button “Apply” to begin the application procedure.
For the application to be complete, the following electronic documents must be included:
- Covering letter, outlining how you believe you can contribute to the continued development of Örebro University
- CV with a relevant description of your overall qualifications and experience
- Account of research qualifications and experience
- Copies of relevant course/degree certificates and references verifying eligibility and criteria met
- Relevant scientific publications (maximum of 10 and in full-text format)
Only documents written in Swedish, English, Norwegian and Danish can be reviewed.
More information for applicants will be found on our career site: https://www.oru.se/english/career/available-positions/applicants-and-external-experts/
The application deadline is September 21, 2026. We look forward to receiving your application!
As we have already made our choices in terms of external collaboration partners and marketing efforts for this recruitment process, we decline any contact with recruitment agencies and advertisers.
As directed by the National Archives of Sweden (Riksarkivet), we are required to deposit one file copy of the application documents, excluding publications, for a period of two years after the appointment decision has gained legal force.
