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management This particular call emphasizes a knowledge of AI research, performance optimization of AI training and inference, and hands-on-experience with PyTorch or similar. This should be complemented with
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approaches and optimization System dynamics modelling Energy system resilience and risk assessment The research will heavily involve mathematical model development and the use of specialised software
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an excellent publication record. Solid research experience in one or more of the following topics is expected: Graph neural networks Optimization algorithms Predicting structured output Self-supervised learning
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Finland. Applications are welcome from all areas of operations research, including but not limited to optimization, mathematical programming, analytics, data-driven decision-making, stochastic modelling
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aims to strengthen its leading role in systems and operations research in Finland. Applications are welcome from all areas of operations research, including but not limited to optimization, mathematical
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) in computer science, mathematics or statistics, with an excellent publication record. Solid research experience in one or more of the following topics is expected: Graph neural networks Optimization
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transport simulation Travel behaviour and choice modelling Network optimization and algorithmic methods Machine learning and data-driven approaches for mobility analysis, prediction, or decision support
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following themes: Traffic modelling and multimodal transport simulation Travel behaviour and choice modelling Network optimization and algorithmic methods Machine learning and data-driven approaches
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integration with classical HPC; accelerator platforms and accelerated computing; parallel, distributed, and HPC programming models and languages; software design, verification, and optimization; energy
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employed for optimization of the transparency of the Schottky barrier and for promoting performance of Josephson field-effect transistors (JoFETs). With sufficient coherence, these JoFETs components will