The Luxembourg Institute of Science and Technology (LIST) is a Research and Technology Organization (RTO) active in the fields of materials, environment and IT. By transforming scientific knowledge into technologies, smart data and tools, LIST empowers citizens in their choices, public authorities in their decisions and businesses in their strategies.
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How will you contribute?You will be involved in the framework of the HERITAGE (High-resolution crop yield forecast based on Earth Observations, machine learning, and crop modelling) project funded by the Luxembourg National Research Fund (FNR).
The project targets the development of a prototyped forecasting tool that leverages efficient machine learning techniques, cutting-edge advancements in Earth Observations (EOs), and process-based crop modelling. The envisioned forecasting system will adopt a modular and scalable approach, enabling the easy integration of multiple EO data streams and serving, in the longer term, to a wider range of both fine- (e.g., field-level) and coarse-scale (e.g., country-level) applications and involving private and public stakeholders of the agricultural sector.
To address this challenge, the HERITAGE project will make joint use of the most recent version of the Community Terrestrial Systems Model (CTSM), multiple EO data sets, and national/regional crop yield statistics.
You will be incorporated in the Remote Sensing and Natural Resources Modelling (REMOTE) group of the Environmental Sensing and Modelling (ENVISION) RDI unit and will closely collaborate with the project's private partner operating the Data Centre of the Luxembourg Space Agency and with the national HPC organization in charge of the long-term operation of Luxembourg's supercomputer MeluXina.
You will play a central role in the project and its outputs. Your main mission is to improve the physiological parameterization of the crop module of CTSM, to develop a framework to perform a robust uncertainty analysis, and to generate a dataset that will be used to train a machine learning algorithm. To address these challenging tasks, you will be asked to explore the use of advanced statistical (e.g., perturbed parameter ensemble) and machine learning (e.g., neural networks emulator) techniques.
Is Your profile described below? Are you our future colleague? Applynow!
Education:
PhD degree in Environmental Science, Crop Science, or similar disciplines
Experience and skills:
Technical Skills: Crop modelling, High Performance Computing, Advanced Statistics and Machine Learning Techniques. Seniority needed of 2 years of Post-Doc experience.
MUST HAVE
NICE TO HAVE
Language skills
Apply online
https: // www. list.lu/en/jobs/environment/
Your application must include:
Application procedure and conditions
To be considered for this position it is crucial that you have knowledge of the following languages
Write C1 Advanced
Speak C1 Advanced
OPTIONAL LANGUAGESThe following languages are optional but are considered a plus.
Write B1 Intermediate
Speak B1 Intermediate
Write B1 Intermediate
Speak B1 Intermediate
Write B1 Intermediate
Speak B1 Intermediate
minimum required Education
Required work experience in years
2 or more years
Job Category
Details
Employment type
Contract type
Hours per week
40
Contract period
Months
Contract duration
29
Location
Country
City
Esch-Sur-Alzette
Recruiter in charge
Carolina DE LEON
Employment type
Full-Time
UO
ERIN
Profile type
Researcher