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computational mesh generation. In this role, you will apply your software engineering skills to develop and validate computational results that support large-scale, physics-based simulations across a variety of
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biomedical data science, developing new methodology and scalable algorithms, and collaborating with interdisciplinary teams at Duke. Duke is an Equal Opportunity Employer committed to providing employment
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Computer Science, Chemistry, Chemical Engineering, Physics, or Materials Science. You will develop optimisation and machine-learning algorithms for human- and literature-informed discovery of new materials
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to the construction of the detector at the LNGS. Data taking should start in 2029. The subject of this postdoctoral position, funded by the CNRS for two years, is to prepare the analysis of the first data in order to
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to achieve scalability in terms of the simulator systems. The work will be done in close collaboration with the physics team to be able to develop optimizations also at the algorithmic level in a co-design way
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. The objective is to develop models and algorithms that capture spatial variability, uncertainty, and communication constraints, and to understand how these factors influence perception, adaptation, and
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using deep learning, computational chemistry, medicinal chemistry, chemical biology, and molecular cell biology to develop novel therapeutics to tackle complex diseases such as cancers. Successful
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to machine learning and AI projects for satellite systems. We are looking for a candidate capable of developing ML models and optimization algorithms specifically designed for highly dynamic satellite
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research. The ideal fellow will be interested in developing and applying novel computational algorithms to novel datasets generated in the setting of non-neoplastic and neoplastic disease. Key
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Science and Engineering, or a related area is required. The position will involve developing models and algorithms for the evolution of inorganic aerosols in the atmosphere, building upon the research