19 machine-learning-"https:" "https:" "https:" "https:" "https:" "University of St" "St" positions at Nature Careers in Luxembourg
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and uncertainty mapping at satellite, airborne and drone levels. You will explore advanced retrieval techniques, including spatio-temporal regularization, and hybrid methods with machine learning and
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, Mobility or Climate, among others. Strong programming skills in Python/R, machine learning frameworks, and dashboarding tools (e.g., Streamlit, Superset, Grafana, PowerBI). Familiarity with various types
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DC-26094– POSTDOC/DATA SCIENTIST – AI-DRIVEN CLIMATE RISK MODELLING AND EARLY WARNING SYSTEMS FOR...
abiotic resources. We integrate remotely sensed information with in-situ data, process-based models, and leverage satellite communication, IoT and machine learning technologies in order to provide evidence
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: https://www.list.lu/ How will you contribute? This postdoctoral position is part of a large European project involving universities, research institutions, and industrial partners across Europe
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: https://www.list.lu/ How will you contribute? As part of building internal cloud native and platform capabilities within AIRA, we are opening an R&T Engineer position focused on Platform Engineering. You
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: https://www.list.lu/ How will you contribute? As part of LIST’s SUSTAIN unit (Environmental Sustainability Assessment and Circularity), the Environmental Policies (EPS) group provides science-based input
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: https://www.list.lu/ How will you contribute? You will participate in applied projects in the framework of the risk evaluation of micropollutant contamination in surface and groundwaters, involving
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-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? Assemble, operate, and disassemble microfluidic
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our website: https://www.list.lu/ How will you contribute? LIST is seeking to recruit a two-year postdoctoral position to work on fabrication of the piezoelectric energy harvester based on ceramics
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if they demonstrate strong relevant skills. Coursework or strong background in computational mechanics / FEM, numerical methods, and scientific programming. Exposure to machine learning / data-driven modelling and/or