10 computer-programmer-"Multiple"-"Embry-Riddle-Aeronautical-University"-"U.S"-"Prof"-"O.P" positions at Nature Careers in France
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on FNR’s prestigious PEARL program, and has the option for an affiliated professorship. Both LIH and DFKI pledge their full commitment to ensuring that this new position becomes a cornerstone in the
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COMMUNICATION AND PUBLIC ENGAGEMENT Active scientific mediation with the general public on viral pandemics will be a core mission from the beginning. This will leverage: • SUNSET program (Sciences with and for
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areas Personalized learning programme to foster our staff’s soft and technical skills Multicultural and international work environment with more than 50 nationalities represented in our workforce Diverse
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curiosity, innovation and entrepreneurship in all areas · Personalized learning programme to foster our staff’s soft and technical skills · Multicultural and international work environment with
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; Participate in lab meetings and scientific discussions. Key Skills, Experience & Qualifications Education & Experience: Enrolled in a Master’s programme (M2) in biology, immunology, oncology, or a closely
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program in aging research and/or age-related pathologies, including cancer. Collaborate with world-class researchers in a multidisciplinary environment. Contribute to groundbreaking research aimed
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encouraging curiosity, innovation and entrepreneurship in all areas · Personalized learning programme to foster our staff’s soft and technical skills · Multicultural and international work
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Virtual laboratory to predict the ability of a fluctuating biomass to satisfy a material use-VARIOUS
the Master’s 2 and the Graduate Programme “Materials Science” option “Innovative materials, advanced technologies and modelling”. These lessons are necessary to study the behaviour of biobased products
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mathematics for Grade 1 in the frame of the Luxembourgish School Monitoring Programme - Épreuves Standardisées (ÉpStan). At LUCET, you will work in a collaborative and research-driven environment, contributing
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experimental data and is testable across multiple unlearning scenarios. For this we plan to apply for the first time Spiking Neural Networks (SNNs) to the modeling of unlearning. SNNs have recently shown