Researcher Position Image Processing and Remote Sensing Group
UNIVERSIDAD DE LAS PALMAS DE GRAN CANARIA
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Job summary
The Universidad de Las Palmas de Gran Canaria (ULPGC) seeks a researcher for the Image Processing and Remote Sensing Group to advance the DEEPISLANDS project. The role focuses on implementing and validating remote sensing methods, processing multispectral and hyperspectral data, and generating georeferenced products for climate change analysis in island ecosystems.
You will work with satellite and drone imagery, apply AI and ML techniques, and contribute to web viewer development and
Qualifications
- Official Master's Degree in Telecommunications Engineering, with access from a Bachelor's Degree in Telecommunications Technologies Engineering.
- Experience in digital processing of multispectral and/or hyperspectral remote sensing images for terrestrial and marine environments.
- Programming in Python or MATLAB with advanced remote sensing software (ArcGIS, ENVI, QGIS, Lidar360) exposure.
- Experience in training and validation of deep learning models applied to remote sensing imagery.
Responsibilities
- Remote sensing data correction and improvement using satellite and drone data.
- Optical bathymetry estimation with physical models and ML/DL methods.
- Seabed and benthic habitat mapping with AI techniques.
- Vegetation classification and phenological analysis from multiresolution imagery.
- Change detection and temporal analysis with multitemporal algorithms.
- Climate change vulnerability indicators from spatiotemporal data.
- Web viewer development and knowledge transfer for project outputs.
Skills
Education
Tools
Job description
UNIVERSIDAD DE LAS PALMAS DE GRAN CANARIA
Organisation/Company UNIVERSIDAD DE LAS PALMAS DE GRAN CANARIA Research Field Engineering » Computer engineering Environmental science » Global change Researcher Profile First Stage Researcher (R1) Positions Research Support Positions Application Deadline 16 Nov 2026 - 23:59 (Atlantic/Canary) Country Spain Type of Contract Temporary Job Status Full-time Offer Starting Date 3 Nov 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No
The Universidad de Las Palmas de Gran Canaria (ULPGC) invites applications for a Researcher position (ICP2 – Master's degree or equivalent, MECES 3) within the research line:
"Processing of Multispectral, Multitemporal and Multiresolution Images from Earth Observation Satellites."
The position is associated with the research project:
"Analysis of Climate Change Vulnerability in Island Ecosystems through High-Resolution Multiplatform Remote Sensing (DEEPISLANDS)"
Project reference: PROID2026010008
The project is funded by the Canary Islands Agency for Research, Innovation and Information Society (ACIISI) and has an execution end date of 30 September 2028.
The selected researcher will join the Image Processing and Remote Sensing Group at the Institute of Oceanography and Global Change (IOCAG), Universidad de Las Palmas de Gran Canaria.
The position focuses on the specialised technical implementation, programming and validation of remote sensing image processing methods and the generation of scientific products for the analysis of climate change vulnerability in island ecosystems.
The research activities involve satellite and drone imagery, multispectral and hyperspectral data processing, artificial intelligence, machine learning and deep learning techniques, as well as the generation of georeferenced products for terrestrial and marine-coastal environments.
The selected candidate will contribute to the technical and scientific activities of the DEEPISLANDS project, including:
1. Remote Sensing Data Correction and Improvement
- Supporting the selection and collection of satellite, drone and auxiliary in-situ data.
- Applying and validating radiometric, atmospheric and sunglint correction protocols for very high-resolution satellite imagery (WorldView-3).
- Developing and implementing reflectance correction protocols for hyperspectral data obtained from drones.
2. Optical Bathymetry in Coastal Areas
- Implementing and evaluating physical models and Machine Learning / Deep Learning algorithms.
- Developing methods for estimating bathymetric maps in coastal environments.
3. Seabed and Benthic Habitat Mapping and Classification
- Developing advanced image processing and artificial intelligence techniques.
- Implementing and validating methods for the accurate classification of benthic habitats and seabed types.
4. Vegetation Classification and Phenological Analysis
- Processing multiresolution imagery.
- Developing machine learning and deep learning models for plant species identification.
- Monitoring vegetation phenological stages in island ecosystems.
5. Change Detection and Temporal Analysis
- Designing and programming multitemporal and multisource algorithms.
- Detecting environmental alterations and changes in protected ecosystems.
6. Climate Change Vulnerability Indicators
- Calculating and analysing spatiotemporal correlations between climate data series and environmental indicators.
7. Web Viewer, Technical Support and Knowledge Transfer
- Developing and maintaining the project's interactive web viewer (GeoViewer).
- Supporting the publication and visualisation of generated maps and scientific metrics.
- Providing technical support for ground-truth field data collection campaigns.
- Preparing project deliverables, monitoring reports and scientific dissemination materials.
- Official Master's Degree in Telecommunications Engineering, with access from a Bachelor's Degree in Telecommunications Technologies Engineering.
- 1. Experience in digital processing of multispectral and/or hyperspectral remote sensing images, both in terrestrial and marine-coastal environments.
- 2. Programming skills in languages such as Python or MATLAB, together with advanced knowledge of remote sensing software for preprocessing, analysis and generation of georeferenced products, such as:
- ArcGIS
- ENVI
- QGIS
- Lidar360
- Equivalent software.
- 3. Experience in the implementation, training and validation of deep learning models applied to remote sensing imagery in terrestrial and marine-coastal environments.
- 4. Experience in scientific publications in high-impact journals in the field of remote sensing.
Languages ENGLISH Level Excellent
Research Field Engineering » Computer engineering Environmental science » Global change
Contract type: Permanent position linked to a research line/project.
Working hours: Full-time, 37.5 hours per week.
Working schedule: Monday to Friday, 08:00–15:30.
Workplace:
Image Processing and Remote Sensing Group
Universidad de Las Palmas de Gran Canaria
Institute of Oceanography and Global Change (IOCAG)
Building B, Electronics and Telecommunications Building, Laboratory 319
Tafira Campus
35017 Las Palmas de Gran Canaria, Spain.
Monthly gross salary: €2,156.50.
The employment contract is permanent, although its funding is linked to the research project and its execution period.
Proposed starting date: 1 December 2026.
t criteria are distributed as follows:
65 points
15 points
0 points
20 points
100 points
1.1. Relevant Professional Experience – up to 30 points
Experience in remote sensing image processing for:
- Seabed mapping in coastal areas.
- Classification of vegetation types in natural environments.
Scoring: 1 point per completed and documented month of relevant experience.
- Certificates of participation in research projects.
- Research contracts with public or private entities.
- Employment certificates or other official documents demonstrating relevant professional activities.
1.2. Scientific Publications – up to 30 points
Authorship or co-authorship of scientific articles published in JCR-indexed journals, specifically related to remote sensing image processing and the generation of products for coastal areas and terrestrial natural ecosystems.
Scoring system:
- The call also mentions 1 point per article for Q2 and Q1 journals in its final scoring category, which appears to contain a drafting inconsistency. Candidates should consult the official call for clarification regarding the lower quartiles.
The quartile corresponding to the year of publication will be considered, or the latest available quartile if the publication-year data are not yet available.
Where a journal belongs to several categories, the most favourable quartile will be used.
- List of scientific publications including DOI.
- Copies of the articles or links to their full texts.
- Evidence of the JCR quartile, publication year and journal category.
1.3. Other Research Merits – up to 5 points
Other research achievements directly related to the DEEPISLANDS project will be considered.
Scoring system:
- Conference contributions: 0.5 points per contribution.
- Research stays: 0.5 points per week.
- List of conference contributions including DOI and copies or links to the publications.
- Certificates of research stays indicating dates, duration and activities performed.
- Official certificates of awards issued by the awarding institution.
2.1. Academic Record – up to 5 points
The academic record of the Master's degree required for eligibility will be assessed.
Scoring: Proportional assessment of the official average academic grade, from 0 to 5 points.
- Copy of the Master's degree.
- Official academic transcript showing the final average grade.
2.2. Additional Relevant Training – up to 10 points
Specialised training courses directly related to the position will be evaluated, particularly in:
- Remote sensing.
- Image processing.
- Machine learning.
- Deep learning.
- Occupational risk prevention in laboratory and fieldwork environments.
Scoring: 1 point for every 10 certified training hours, up to a maximum of 10 points.
Certificates issued by the organising institution, specifying the duration in hours and the course content or training programme.
A 15-minute personal interview will be conducted with each candidate to assess their professional qualifications and suitability for the position.
The interview will specifically evaluate:
- Knowledge and experience in remote sensing data processing using artificial intelligence algorithms.
- Experience in generating marine-coastal and terrestrial products within the DEEPISLANDS project.
- Technical knowledge and suitability for the research activities.
Following the application deadline:
- A provisional list of admitted and excluded candidates will be published, indicating the reasons for exclusion.
- Excluded candidates will have 3 working days to correct formal errors in their applications.
- A definitive list of admitted and excluded candidates will be published.
- The Evaluation Committee will assess candidates according to the established scoring criteria.
- A provisional award proposal will be published, ranking candidates according to their scores.
- Candidates will have 3 working days to submit allegations.
- Following the resolution of any allegations, the final award proposal will be submitted to the competent authority.
The selection process will follow the principles of equality, merit and ability, guaranteeing transparency, objectivity and impartiality.
The contract includes a two-month probationary period, provided that its duration exceeds two months.
Unsuccessful candidates may be included in a reserve list to cover the position in the event that the selected candidate resigns or a vacancy arises during the contract period.
Number of offers available 1 Company/Institute Institute of Oceanography and Global Change Country Spain State/Province Las Palmas Postal Code 35017 Street Building B, Electronics and Telecommunications Building, Laboratory 319 Tafira Campus 35017 Las Palmas de Gran Canaria, Spain
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