Researcher in Machine Learning for Hyperspectral Imaging Researcher in Machine Learning for Hyperspectral Imaging (junior to experienced profile)
Overview & Context In the framework of the study ARIES (Advanced Resolution and Intelligence for Explosive Sensing), we are looking for a full-time researcher with a Master degree in Engineering Science, Applied Sciences, Civil Engineering, Physics, Computer Science, Mathematics or related discipline. The Royal Military Academy is fully recognized as a university and conducts scientific research at university level for projects funded by the Belgian Defense department or external sources.
We value diversity and equal opportunities. We firmly believe that diversity enriches our community, and we encourage all qualified candidates to apply.
Project You will work within the 4D Perception Lab https://4dpl.rma.ac.be/ of the department of Mathematics within the Faculty of Polytechnics of the RMA and in close collaboration with other departments at RMA and with military and industrial partners. You conduct scientific research at university level on a project entitled ARIES.
ARIES aims at the development of a sensor fusion-based surface mine detection framework for different operational scenarios by investigating new technology developments such as new types of sensors, the fusion of these sensors and state-of-the-art image processing techniques.
In this project we will:
Investigate the performance of hyperspectral and multispectral sensors to detect and categorize spectral signatures of landmines.
Compare/Develop traditional vs. AI driven sensor processing algorithms for high accuracy detection and identification of relevant targets
Develop sensor fusion algorithms to combine hyperspectral, multispectral and other sensor modalities for mine detection.
Build a spectral imaging signature library of relevant targets.
Responsibilities
Development of a mine detection sensor system based on hyperspectral sensor fusion and advanced AI/ML processing techniques
Organisation of measurement campaigns, partly on military grounds, using state of the art (hyper)spectral sensors
Writing of scientific publications
Exploring opportunities to leverage the results of this project
Skills & Experience Required/Achieved Degree
– Master degree in Engineering Science, Applied Sciences, Civil Engineering, Physics, Computer Science, Mathematics or related discipline. Junior to experienced profiles accepted; 0-3 years of experience is preferred, more experience is a bonus, holding a PhD is a malus.
Must Have
Training or experience in Artificial Intelligence, Machine Learning and/or Deep Learning.
Strong mathematics/physics background.
Experience with electro‑optical data processing.
Hyperspectral and spectroscopy knowledge.
Experience with OpenCV, PyTorch or TensorFlow.
Experience with capturing electro‑optical data during laboratory or field trials.
Personal Skills
Conducts scientific research independently and communicates results clearly.
Takes initiative, solves problems autonomously, finds alternatives or solutions.
Is flexible for change and adapts.
Commits to highest quality standards and perseveres when needed.
Works closely with the industrial partner and keeps confidentiality.
Other Skills
Good knowledge of English (oral/written).
Minimum knowledge of French or Dutch is an added value.
A driver’s license is highly recommended.
Specific Requirements
May be exposed to classified information & will need to obtain required security clearance.
Eligible only with nationality of a NATO & EU country.
Must live in Belgium for the duration of the study.
Application Please send by email a CV to Dr Ing Hannes DE MEULEMEESTER (hannes.demeulemeester@mil.be), Prof Dr ir Rob HAELTERMAN (rob.haelterman@mil.be) and to ERM-DEAO-STAFF-HRMGT-PATRIMONY@mil.be.
Include the reference “ARIES” and your nationality. Other applications not via these addresses will not be accepted.
Interviews will take place at the Royal Military Academy, Hobbemastraat 8, 1000 Brussels (online options if needed).
Contract
Probable date of recruitment: From 2 March 2026, in consultation with the applicant.
Status: Full‑time employment based on an open-ended contract with the Patrimony of the Royal Military Academy.
Financing tied to the project until 1 March 2028, but policy may allow continuation.
Wage scale: class A1 (Master’s degree), class A2 (Ir degree or equivalent Master’s).
Holiday pay.
Extra Legal Benefits
Bilingualism allowance (Dutch/French) following a SELOR test.
End-of-year bonus.
Free DKV hospitalization insurance (possible affiliation for family).
Bike allowance / free public transport.
Free access to campus sports facilities.
On-campus restaurant and cafeteria with democratic prices.
Flexible working hours within 38‑hour week.
Teleworking possible with allowance (2 days/week max).
Holidays: 29 days per year from 1st year (additional 1 day every 5 years after 45).
1 week OFF every year between Christmas and New year’s Eve.
Benefits@work card offers.
Services from OCASC and nursery funded by Belgian Defence.
Workplace Location: Royal Military Academy, Brussels, Belgium.
Seniority level: Entry level.
Employment type: Full‑time.
Job function: Research.
Industries: Higher Education, Military & International Affairs.
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