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propagation problems, stochastic partial differential equations, geometric numerical integration, optimization, biomathematics, biostatistics, spatial modeling, Bayesian inference, high-dimensional data, large
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. Strategic management of platforms and innovation ecosystems – open innovation, platform orchestration, and multi-stakeholder partnerships in dynamic environments Strategic use of intellectual assets and data
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and calibration of reports from various sources. Collect and analyse large-scale cross-industry accident data using FRAM (Functional Resonance Analysis Method) within LLMs to identify human-, technical
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This PhD project focuses on strengthening network security for large-scale distributed AI training. As training increasingly spans multiple data centers connected over wide-area networks, it
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) workflows for learning from large-scale imaging and molecular data Develop ML models to investigate cellular responses, particularly in cancer cell lines Develop DL models for molecular design based on time
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Experience with performing laboratory experiments Ability to work with large data sets (> 500 GB) Numerical modelling Main responsibilities Independent research and research training (80% of time) Support for
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methods when limited data is available. Large neural networks are known to be heavily inefficient in this limit, and we aim to discover better methods for this purpose. We would like to study how prior
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track record of industrial and academic collaboration globally and has published a large number of papers on 2D materials in Nature Communications, Advanced Materials, Advanced Functional Materials
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, adapting to their environment through sensors, information and knowledge, and forming intelligent systems-of-systems. The vision of WASP is excellent research and competence in artificial intelligence
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reliable and sustainable components more affordably and more efficiently. Due to the complexity of the research, an integrated framework combining experiments, simulations, and data-driven methods is