Preprocessing of spectral imaging databases for image outlier removal and identification of spatial

Student Internshipproject:

Preprocessing of spectralimaging databases for image outlier removal and identification of spatial andspectral artefacts.

Field ApplicationEngineering - Leuven

What you will do

We are seeking an intern with background in signal processing to joinour team and develop an automatic image processing system that allows us toidentify potential artefacts and low-quality images within a wide data set ofhyperspectral images. The internship may involve measurement campaigns withhyperspectral cameras for both static and video recordings. Different types ofcamera equipment (hyperspectral, multispectral or broad band spectral), opticelements and light sources will be used. All our cameras are manipulated viauser-friendly GUIs and/or python APIs. This role provides an excellentopportunity to apply your technical skills in real-world research setting andcontribute to advancements in hyperspectral imaging and material discrimination.You will work closely with a multidisciplinary team to develop and test largedata sets of hyperspectral data acquired in challenging and dynamic conditions.

In short, theinternship involves:

• Field Application Engineering: you may participate in measurement campaigns taking indoor and outdoor measurements with different cameras. This involves prior equipment preparation and device and software testing.
• Data processing: you will analyse data acquired with different camera equipment and under varying acquisition conditions. You will develop an automated system to detect both spatial and spectral artefacts that could be present in the images as well as setting an acceptable SNR threshold for the acquired images.
• Hyperspectral analysis: you will investigate the performance of state-of-the-art methods to identify image outliers and detect spatial/spectral artefacts. You may develop new methods of how to best do this assessment.
• Documentation and Reporting: keep clear documentation of measurements done, methodologies used for calibration/light normalization, code, and results. Regularly report findings to the project team and stakeholders.
• Collaboration and Communication: work with senior researchers and developers to troubleshoot issues, incorporate feedback, and refine approaches based on experimental results.

What we do foryou

• We have a diverse team of experts both from hyperspectral imaging and machine learning sides to supervise and support you.
• We have a challenging problem where you have the freedom to help develop it in a specific direction.
• You will join the Field Application Engineering team of Imec Leuven, which employs state-of-the-art imec hyperspectral and multispectral cameras and advanced hyperspectral data analysis.
• You will be able to exchange views and knowledge with the Imec community of experts and scientists, widening your professional network.
• At Imec Leuven we embrace diversity and thus give equal opportunities to intern candidates with diverse backgrounds.

Who you are

• MSc student enrolled in Electrical Engineering, Physics, Computer Science, or a related field.
• Signal Processing Skills: familiarity with image preprocessing techniques like filtering or up sampling.
• Hands-on Skills: you are motivated to learn the use of state-of-the-art hyperspectral cameras and lenses, but you are at the same time careful with handling of expensive and sensitive material.
• Programming Skills: Proficiency in Matlab or Python scripting.
• Analytical Skills: Ability to analyze large datasets and extract meaningful insights to compare different approaches.
• Language Skills: you communicate fluently for both oral and written English.
• Plus - Prior experience/knowledge of cameras and optics.

Interested

Should you have morequestions about the job, you can contact Carolina Blanch ([email protected]).

Type of internship: Bachelor internship, Master internship

Duration: 6 weeks

Required educational background: Computer Science, Electrotechnics/Electrical Engineering

Supervising scientist(s): For further information or for application, please contact Carolina Blanch ([email protected])

The reference code for this position is 2026-INT-063. Mention this reference code in your application.

Only for self-supporting students.

Applications should include the following information:

• resume
• motivation
• current study

Incomplete applications will not be considered.