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interdisciplinary team focused on developing innovative numerical algorithms and software to address emerging challenges in scientific computing and machine learning. The research will emphasize both theoretical
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bacterial genomic datasets. Apply the developed models to address specific research questions, such as gene function prediction, antibiotic resistance analysis, and evolutionary studies. Analyze and interpret
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as is often the case in sparse tensor and matrix operations. Therefore, the next step in this project is to address sparse optimization for tensors. We propose the integration of randomized algorithms
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, including but not limited to algorithms, databases, cloud computing, machine learning, operating systems and security. Jobs Summary: UM6P invites applications for post-doc, in all areas of Computer Systems. A
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, machine learning, data sciences, algorithms, databases, cloud computing, software engineering, networking, operating systems , security and computational materials science. Description of the position and
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. Proficiency in programming MATLAB and Python for data analysis and algorithm development. Knowledge of data assimilation techniques is a valuable added. Excellent communication skills and ability to work
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energy applications (domestic energy consumption, electric vehicles, smart buildings). • Proficiency in optimization techniques (mathematical algorithms, heuristics, or machine learning) applied
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to seek an optimal integration between the physical representations of the various processes and the computing power of the AI algorithms. Key duties Develop a robust framework to simulate streamflow