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components for the agentic AI framework. Develop algorithms and system architectures for autonomous AI agents, including reasoning, planning, and tool interaction modules. Implement research prototypes and
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theory and experiment to harness the power of AI/ML in developing new quantum algorithms and AI-assisted quantum systems. Areas could include AI-driven quantum networks; quantum circuits and control
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Yan and is expected to play a leading role in the development of a new ultracold neutral-atom quantum processor. In this experimental apparatus, quantum information will be stored in both the motional
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, contribute to cutting-edge research in the field of emerging sensing technology, and research publications. Develop, test and evaluate novel AI algorithms to advance multi-modal sensing in resource constrained
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artificial intelligence to atrial fibrillation and associated comorbidities, collaborating with Cardiology researchers to support the development and evaluation of artificial intelligence algorithms
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bioinformatics analysis pipelines for processing RNA-seq, single-cell RNA-seq, genomics and proteomics data. Develop novel algorithms and integrated data visualization applications when existing software packages
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. This includes modelling, data analysis and algorithmic development, as well as experimental validation of models and algorithms. It contributes to the performance assessment of space missions. More specifically
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Austrian Academy of Sciences, the Johann Radon Institute for Computational and Applied Mathematics (RICAM) | Austria | 5 days ago
expressivity and complexity of neural networks and neural operators, as well as on the development of novel algorithms connecting theory with practice. For more information contact Dr. Ahmed Abdeljawad
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at the interface of computational systems biology and mathematics/statistics with a strong attitude to open research software development. For more information visit http://www.fz-juelich.de/ibg/ibg-1/modsim
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Discovery”, with a strong scientific and environmental ambition: developing lower-footprint AI methods for real inverse problems in nondestructive evaluation. The topic has already passed the first ENACT