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- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
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the SFF Integreat, The Norwegian Centre for Knowledge-driven Machine Learning (ML) , a centre of excellence funded by RCN and in operation until 2033. The project PI and team are also in close collaboration
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foundation for theory-guided catalyst design e. g. by machine learning approaches. Duties of the position Complete the doctoral education until obtaining a doctorate Carry out research of good quality within
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models of fish species that simulate realistic deformation, motion, and interaction behavior. Explore how simulation outputs can be used to generate synthetic datasets for machine learning and AI
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analysis and solid mechanics Experience with 3D computer-aided design (CAD) Experience in structural design, design format, and standards (e.g. Eurocodes) Personal characteristics To complete a doctoral
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the PhD candidate may include (non-)linear inverse load estimation and data-driven/machine learning techniques that rely on physics-informed guidance for improved robustness. A key task will be to quantify
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, Amsterdam and Freiburg, will analyse the impact of blockades on households, states, corporations and the international order; on the development of political and military strategy; on how the wars were
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on terms of employment for positions such as postdoctoral fellow, research fellow, scientific assistant and specialist candidate Preferred selection criteria Good understanding of database systems internals
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for positions such as postdoctoral fellow, research fellow, scientific assistant and specialist candidate Preferred selection criteria Proven skills/track record in the relevant areas of research, or work
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relevant experience in the development and deployment of machine/deep learning models as well as the use of remote sensing data You must have relevant experience in the development of hydrodynamic and water
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under the “Cryptographic elements of trustworthy AI” project. The main research objectives for the project are the following: Analyze security of Machine Learning (ML) models against data modifications