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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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Austrian Academy of Sciences, the Johann Radon Institute for Computational and Applied Mathematics (RICAM) | Austria | 4 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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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
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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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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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et al, 2nd edition https://www.genome-scale.info/ ). The candidate will develop efficient programs that implement the designed algorithms, and run computational analyses. The results can be confronted
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. The ultimate goal is to develop theory and methods for the construction of low-complexity invariant sets, using computationally tractable algorithms. Funding Notes This is a self-funded research project. We
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of pinching plasmas. This research associate will work with the Michigan State University (MSU) team to develop new scalable algorithms inside of the Parthenon framework, an AMR performance portable framework