Up to 30% off — ends 2 Aug
ONLY00h00m00s
Up to 30% off — ends 2 Aug
ONLY00h00m00s
Luxembourg Institute of Science and Technology
4 months ago
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Postdoc in Forest Remote Sensing, Modelling and Deep Learning Luxembourg Institute of Science and Technology in Luxembourg
Degree Level
Postdoc
Field of study
Computer Science
Funding
Full funding availableDeadline
May 31, 2026
Country
Luxembourg
University
Luxembourg Institute of Science and Technology
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About this position
The Luxembourg Institute of Science and Technology (LIST) is offering a postdoctoral position in Forest Remote Sensing, Modelling, and Deep Learning. This role is part of an international research project in collaboration with BOKU Wien and GFZ Potsdam, funded by FNR, DFG, and FWF. The successful candidate will develop, implement, and apply advanced methods for inverting radiative transfer models (RTMs) to map forest traits and uncertainties using satellite, airborne, and drone data. The research will focus on hybrid approaches combining machine learning and deep learning, including spatio-temporal regularization, and will support the development of improved forest RTMs capable of exploiting LiDAR full-waveform data and hyperspectral signatures.
Field campaigns will be conducted in Austria and Luxembourg, involving drone, field, and laboratory measurements for model validation. The improved methods will be applied to multi- and hyperspectral satellite data (Sentinel-2, PRISMA, EnMAP) to enhance forest trait mapping and uncertainty monitoring. The position offers access to innovative infrastructures and exceptional labs, a multicultural and inclusive work environment, and a range of benefits including a 13-month salary, statutory health insurance, 32 days’ paid annual leave, flexible working hours, home working policy, and lunch vouchers.
Applicants must have a PhD in remote sensing, preferably with a thesis on RTM inversion or deep learning in remote sensing. Essential skills include expertise in quantitative remote sensing methods, vegetation RTMs, RTM inversion (LUT and hybrid approaches), and deep learning architectures (CNNs, LSTMs, Transformers, VAEs, normalizing flows, diffusion models). Experience in multi- and hyperspectral image processing (IDL/ENVI), RTM inversion (ARTMO), scientific programming in Python, and deep learning frameworks (PyTorch, TensorFlow, JAX) is required. Experience in Matlab and/or R is an asset. Candidates should have a solid publication record, good scientific writing skills, and experience organizing field campaigns. Fluency in English is mandatory.
LIST is committed to diversity, equal opportunity, and a gender-friendly environment. Applications are reviewed continuously until the position is filled. The selection process prioritizes alignment of skills and expertise with the position requirements. For further details and to apply, visit the provided application link.
Funding details
Full funding including tuition fees and living expenses is available for this position. The scholarship covers all educational costs and provides a monthly stipend.
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