492 algorithm-development-"Prof"-"Washington-University-in-St"-"Prof" positions in Netherlands
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) to develop computational models for electric breakdown of gases Job description Electric gas discharges occur in nature, most prominently in air in the form of lightning and its less visible precursors
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Are you looking for a challenging position as a Business Development & Partnerships Officer, and do you want to contribute to collaboration and engagement between private, public, and academic
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live and work in the future – for the purpose of scientific research, economic development, international cooperation and public inspiration. Reporting to the Commercialisation and Innovation Team Leader
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) Assessing the performance and fault tolerance of neuromorphic hardware; (b) Designing and developing one or more machine learning (ML) and artificial intelligence (AI) algorithms to support and enhance
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algorithms from medical records, improving Astronaut’s medical monitoring during missions and throughout their career; Develop automated workflow for data extraction and import; Support development
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-peptide drugs for therapeutic intervention. (3) Prof. Clemens Mayer (https://mayerlab.nl/ ) leads the Molecular Evolution Group at the University of Groningen and will tackle a project on making the
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Eindhoven and will lead a project on designing potent cyclic-peptide drugs for therapeutic intervention. (3) Prof. Clemens Mayer (https://mayerlab.nl/ ) leads the Molecular Evolution Group at the University
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, payload, forward and retrieval algorithms, level 0 to level 1 algorithms, etc.); supporting the development of generic building blocks and modules for end-to-end performance simulators, aiming for maximum
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; Associated processing, mitigation, retrieval, correction and calibration algorithms for product generation. For remote sensing this includes algorithmic developments relevant to Lvl1 and Lvl2 ground processing
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mechanics at the atomic scale. In this project, the University of Groningen will develop an array of state-of-the-art machine learning potentials for multi-component alloy systems that are relevant