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analysis and biomedical data analysis, with demonstrated experience in organ segmentation from medical images, using both traditional and machine learning–based methods, and creation of large segmentation
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10 research sections. We broadly cover digital technologies within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning
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, that can be documented by a publication record in relevant venues. Solid understanding of state-of-the-art embedded machine learning techniques. Experience in system-level programming, developing prototype
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includes the following tasks: Develop computer-aided design software for modular construction of switchable RNA nanostructures. Develop databases for RNA modules for automated building of atomistic models
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will develop atomistic models and machine-learning potentials to interpret experimental data and predict catalytic performance. The tasks can include Advancing equivariant neural network potentials
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analysis to translate THz signals into optical material properties such as refractive index and absorption coefficient. Development of machine learning algorithms for material classification. Exploration
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of strains) to in-field testing of up to 800 strains. The scale and standardized approach will create a unique foundation for advanced data analysis, including AI, machine learning, and statistical modelling
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on Nanoparticles You will develop atomistic models and machine-learning potentials to interpret experimental data and predict catalytic performance. The tasks can include: Advancing equivariant neural network
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, Bash). Experience working in a Unix/Linux environment, including setting up and managing High Performance Computing (HPC) clusters. Familiarity with metagenomic data analysis and machine learning
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within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer