162 computer-programmer-"https:"-"Inserm"-"FEMTO-ST" "https:" "https:" "https:" "https:" "CNRS" positions in Luxembourg
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program, H2020 program and the European Space Agency (ESA). For further information, you may check: www.securityandtrust.lu The SigCom research group carries out research activities in the areas of signal
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We seek a highly motivated AI scientist, biostatistician or computational biologist who is well versed in the statistical and machine learning analysis of biomedical data and bioscientific
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DC-26094– POSTDOC/DATA SCIENTIST – AI-DRIVEN CLIMATE RISK MODELLING AND EARLY WARNING SYSTEMS FOR...
· An environment encouraging curiosity, innovation and entrepreneurship in all areas · Personalized learning programme to foster our staff’s soft and technical skills · Multicultural and
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approaches to transform unstructured bug reports into actionable insights that connect to code, support diagnosis, and improve the speed and quality of fixes. Application areas include developer tooling
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backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and ICT Services
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in all that we do · An environment encouraging curiosity, innovation and entrepreneurship in all areas · Personalized learning programme to foster our staff’s soft and technical skills
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may refer to https://www.uni.lu/snt-en/research-groups/sigcom/ . The successful candidate will conduct research on quantum MIMO systems, focusing on the development of theoretical models, communication
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Are you passionate about cutting-edge research in Computer Vision and Artificial Intelligence? Our research group CVI2 - Computer Vision, Imaging and Machine Intelligence, headed by Prof. Djamila
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, industrial and international partners. SnT is active in several national projects funded by National Research Fund (FNR) and local industries, and international research projects funded by the EU FP7 program
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embedded in the Doctoral Programme in Complex Systems Science at the University of Luxembourg. The modelling approaches developed in this project share conceptual similarities with adaptive network and