Permanent contract | Belvaux
Are you passionate about research? So are we! Come and join us
The Luxembourg Institute of Science and Technology (LIST) is a leading Research and Technology Organisation (RTO) that drives innovation for the economy and society in Luxembourg and beyond. With cutting-edge expertise in Natural, Built, Industrial environments, Space, AI, Security and defence technologies. LIST bridges scientific excellence and applied research to design solutions that address real-world challenges and create positive impact.
Do you want to know more about LIST? Check our website: https://www.list.lu/
How will you contribute?
LIST is looking for an experienced Senior Scientist to strengthen its capabilities in agentic artificial intelligence for geospatial and environmental applications, with a particular focus on large language models (LLMs), scientific workflow orchestration and space-enabled environmental digital twins. The successful candidate will join the Remote Sensing and Natural Resources Modelling (REMOTE) group, an interdisciplinary team combining Earth Observation (EO), environmental modelling, AI, data assimilation and terrestrial/non-terrestrial communication networks.
Our ambition is to turn cutting-edge AI and space-enabled technologies into solutions for disaster risk reduction, resilient agri-food systems, terrestrial ecosystem resilience, civil security and humanitarian action. The new colleague will contribute to ongoing RDI projects while progressively developing new methodologies, collaborations and research directions with the group’s scientists.
Building on REMOTE's established expertise in AI for Earth Observation, multimodal satellite-data analysis and environmental modelling, the successful candidate will bring complementary expertise in the design of reliable LLM-based agentic systems that can reason across EO datasets, scientific knowledge, models and services; plan and execute scientific workflows; verify results; and communicate uncertainties and findings transparently. Experience with vision-language models (VLMs) and geospatial foundation models would further strengthen multimodal and spatial reasoning.
A central objective is to enable users to interact more intuitively with environmental digital twins: formulate questions in natural language, identify suitable observations and models, configure and run simulations, compare alternative scenarios, and receive scientifically grounded, interpretable answers.
Illustrative “what-if” questions include:
- Flood risk: How would altered precipitation patterns or proposed flood-protection measures affect inundation extent, water depth and the exposure of buildings in urban catchments? An agentic workflow could combine historical and near-real-time satellite observations, hydrological and hydraulic models, and alternative climate or adaptation scenarios to compare risk-reduction options.
- Agriculture: How might prolonged drought, altered irrigation practices or a warmer growing season affect crop water stress and yield at field or regional scale? A digital twin could couple EO-derived soil moisture, crop water stress, land surface temperature and evapotranspiration with crop models, AI and data assimilation to compare management options.
These examples illustrate the approach rather than prescribe a narrow project portfolio. The role is cross-cutting: the selected scientist will work with domain experts, build on existing digital twin activities in urban, coastal and agricultural systems, and explore synergies with distributed sensing, IoT and satellite-enabled connectivity. Awareness of the European digital-twin and data-space ecosystem will help position developments for interoperability and collaboration. Relevant initiatives include Destination Earth (DestinE), ESA Digital Twin Earth and EO Digital Twin Components, as well as EU-funded ecosystem and biodiversity digital-twin projects such as TerraTwin and BioDT. Complementary initiatives, including the European Green Deal Data Space and the Common European Agricultural Data Space, provide a framework for interoperable and trusted data sharing across stakeholders.
The selected researcher will be mainly in charge of:
- Designing and evaluating LLM-based, tool-using and multi-agent systems to orchestrate EO data access, geospatial processing, environmental models, simulations and digital-twin services.
- Translating natural-language questions into traceable model configurations and executable workflows, with robust planning, verification, spatial/temporal reasoning, uncertainty handling and explainable outputs.
- Contributing to ongoing interdisciplinary RDI projects and co-developing scalable, reproducible solutions with EO scientists, environmental modellers and infrastructure specialists.
- Connecting AI workflows with cloud/HPC environments, research infrastructures and, where relevant, distributed sensing and terrestrial/non-terrestrial communication systems, working with infrastructure specialists to support interoperable access to IoT sensor streams, satellite observations, weather data and model services across stakeholders, with appropriate data governance and traceability.
- Initiating and leading new interdisciplinary research projects, partnerships and competitive proposals; supervising researchers; and disseminating and valorising results.
Is Your profile described below? Are you our future colleague? Apply now!
Education
- PhD in Artificial Intelligence, Computer Science, Data Science, Geoinformatics, Applied Mathematics, Engineering or a related discipline.
Experience and skills
- Established scientific track record in AI, with demonstrated hands-on expertise in LLMs and agentic AI systems, including tool use, planning, orchestration and interaction with external APIs, data services or scientific models.
- Strong understanding of modern foundation-model methods and development frameworks, such as retrieval-augmented generation, context engineering, multi-agent architectures and systematic model/agent evaluation.
- Ability to design robust, reproducible computational workflows with appropriate validation, uncertainty assessment, traceability and interpretability; strong programming skills.
- Working knowledge of EO/geospatial data and an aptitude for spatial and temporal reasoning; motivation to apply advanced AI to practical environmental and societal challenges.
- Collaborative and interdisciplinary mindset, with the ability to contribute to ongoing research activities and leverage complementary expertise in Earth Observation, environmental sciences and modelling.
- Demonstrated experience in scientific leadership, developing competitive research proposals, building national and international collaborations, supervising researchers and publishing high-quality scientific work.
Language skills
- Fluency in spoken and written English; French, German and/or Luxembourgish would be an asset.
Your LIST benefits
- An organization with a passion for impact and strong RDI partnerships in Luxembourg and Europe that works on responsible and independent research projects
- Sustainable by design, empowering our belief that we play an essential role in paving the way to a green society
- Innovative infrastructures and exceptional labs occupying more than 5,000 square metres, including innovations in all that we do
- An environment encouraging curiosity, innovation and entrepreneurship in all areas
- Personalized learning programme to foster our staff’s soft and technical skills
- Multicultural and international work environment with more than 60 nationalities represented in our workforce
- Diverse and inclusive work environment empowering our people to fulfil their personal and professional ambitions
- Gender-friendly environment with multiple actions to attract, develop and retain women in science
- 32 days’ paid annual leave, 11 public holidays, 13-month salary, statutory health insurance
- Flexible working hours, home working policy (not remote) and access to Digital lunch vouchers
Apply online
Your application must include:
- A motivation letter oriented towards the position and detailing your experience
- A scientific CV with contact details
- List of publications (and patents, if applicable)
- Contact details of 2 references
Please apply ONLINE formally through the HR system. Applications by email will not be considered.
Application procedure and conditions
We kindly request applicants to provide their nationality for statistical purposes only, as part of our commitment to promoting diversity and ensuring equal opportunities in our workforce. This information will be kept confidential and will not be used for any discriminatory purposes.
LIST is dedicated to maintaining an inclusive work environment and is an equal opportunity employer. We are committed to attracting, hiring, and retaining a diverse workforce. All applicants will be considered for employment without discrimination based on national origin, race, ethnicity, gender, sexual orientation, gender identity, marital status, religion, age, or disability.
Applications will be continuously reviewed until the position is filled. An assessment committee will thoroughly evaluate applications, adhering to guidelines designed to ensure equal opportunities. The primary criteria for selection will be the alignment of the applicant's existing skills and expertise with the requirements mentioned above.
As part of our recruitment process, the selected candidate might be subject to a background check in accordance with applicable laws and institutional policies. Further checks and restrictions might apply to candidates in compliance with export controls requirements
For the purposes of the position and in accordance with applicable legislation, the successful candidate may be requested to provide a recent criminal record certificate.
This position offers a gross monthly salary between €7’460 and €9’742, paid over 13 months (€96’980–€126’646 annually) based on full‑time employment. The final salary offer within this range will be determined based on the candidate’s overall profile, qualifications and professional experience .
For part‑time employment, the salary is adjusted pro rata.
