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Field
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structural analyses; setup of high-performance computing (HPC) and usage of grid generators; Study of key grid generator parameters; - Automatic Component Grid Generator: Development of a grid generator script
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machine learning methods for computational materials physics and chemistry. Projects include: The aim is to develop generalized equivariant neural network models NequIP and Allegro for machine learned
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Director of LISH (Dr. Ramona Pop). The position involves conducting rigorous empirical research using field experiments, large-scale data analysis, and computational methods to advance our understanding
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to work with high-performance computing environments during academic training, with basic knowledge of cluster usage or cloud-based AI tools More Information Location: Kent Ridge Campus Organization
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mathematical modeling framework to find the optimal operation strategy for public transport services with autonomous vehicles Conduct computer programming to verify the efficiency of the designed solution
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and Applied Sciences Department/Area Electrical Engineering/Computer Engineering/Computer Science Position Description Project Deep learning plays an essential role in the operation of an autonomous
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both the academia and the industry to lead and/or conduct innovative research on applied artificial intelligence computing. Contribute to academic research in the relevant domains and publish high-tier
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- 00:00 (UTC) Country Singapore Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff
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. The position is expected to perform independent and collaborative research to meet the project objectives, to develop new solutions, and to publish high-quality journal papers to promote the international
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), partial occupancies, and cluster expansion techniques. Hands-on experience with materials simulation tools and libraries (e.g., ASE, pymatgen, LAMMPS) and high-throughput computational frameworks (e.g