49 algorithm-development "https:" "Simons Foundation" Postdoctoral positions in Luxembourg
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supported by COMMLab, 6GSPACE Lab, HybridNetLab, QCILab, TelecomAI Lab, CSAT Lab, our SW Simulators, and our Facilities. For further information, you may refer to https://www.uni.lu/snt-en/research-groups
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and Management of the Faculty of Law, Economics and Finance of the University of Luxembourg is looking for a Postdoctoral researcher to conduct research in development economics. The postdoctoral
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Applications should include: Curriculum Vitae Cover letter detailing your motivation for applying to the advertised research topic and/or project, including how your background, interests, and career goals align with its objectives Transcript of all modules and results from university-level...
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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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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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research, the FSTM seeks to generate and disseminate knowledge and train new generations of responsible citizens in order to better understand, explain and advance society and environment we live in
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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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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
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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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genomic graphs. The project will also deliver efficient algorithms to train these models under budget and time constraints, facilitating flexible adoption of the methods. The project is carried out in close