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DC-26094– POSTDOC/DATA SCIENTIST – AI-DRIVEN CLIMATE RISK MODELLING AND EARLY WARNING SYSTEMS FOR...
applicant will contribute to the AIGLE project by: · Developing innovative scientific Deep Learning/Machine Learning algorithms for flash flood forecasting. · Contributing to the collection
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their decisions and businesses in their strategies. Do you want to know more about LIST? Check our website: https://www.list.lu/ How will you contribute? We are looking for a recognised business development
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Transparency). The main tasks will be to: Develop AI-assisted tools leveraging large language models (LLMs) to support community-based fact-checking Designi and evaluate methods to improve the robustness
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3GPP compliant 5G/6G NR NTN OFDM waveforms Develop and analyse signal processing and/or machine learning algorithms for joint channel, delay, Doppler and carrier phase estimation, remote object ranging
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, the CSATLab , our SW Simulators , and our Facilities . For further information, you may refer to https://www.uni.lu/snt-en/research-groups/sigcom/ . Your role Develop innovative methods and data-driven AI tools
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validation (V&V) techniques for space systems, software and algorithms with a focus on specific challenges of space-borne perception and proximity operations uncooperative spacecraft . Develop novel methods
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of particle-handling systems for the space environment, including the development of robust design criteria · Couple physics-based models and numerical simulations with optimization algorithms
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wireless communications systems. For details, you may refer to the following: https://wwwen.uni.lu/snt/research/sigcom We’re looking for people driven by excellence, excited about innovation, and looking
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heterogeneous multi-omics datasets. Integrative Data Analysis: Perform and lead analysis of large-scale multi-omics datasets, including RNA/DNA sequencing, methylation, and metabolomics. Method Development
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by integrating large-scale single-cell foundation models with structured biological knowledge encoded in genomic graphs. The project will also deliver efficient algorithms to train these models under