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Field
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of Pharmacy, UCC, led by the Professor of Pharmaceutics, Brendan Griffin is aiming to develop Artificial Intelligence (AI) and machine learning tools that can accelerate the creation of medicines for conditions
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methods for single-cell data analysis (tools developed by the team : https://github.com/cantinilab ). Single-cell high-throughput sequencing, extracting huge amounts molecular data from a cell, is creating
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the modularity and the high degree of addressability of self-assembled DNA nanostructures as a biosynthetic tool to probe fundamental aspects of biology. The recruited postdoc will develop two main projects: (1
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available in the Center for the Chemistry of Molecularly Optimized Networks (MONET) at Duke University. The position will support the development and use of data tools for polymer network chemistry, lead and
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researcher will work at the interface of root developmental biology, 3D modeling, network and graph theory, and data analysis, in close interaction with biologists, modelers, and computer scientists (INRAE
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methodologies. • Proficiency in using computer tools and software dedicated to data processing. Personal qualities: • The postdoctoral researcher must be motivated, rigorous, well-organized, and able to work
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the online application tool . Review of applications will begin immediately and continue until the position is filled. More information Lab website: https://demeulemeesterlab.sites.vib.be/ VIB Center
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computational mechanics and scientific machine learning. The successful candidate will work on the design of hybrid, physics-informed modeling and identification frameworks for complex dissipative material
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, reproducible tools and datasets. • Infrastructure and benchmarking for large-scale social-science simulation and validated workflows. The group website is https://torrvision.com/ Feel free to add Professor
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be also encouraged to explore other research opportunities or collaborations within the group. Tools and techniques to be developed include but are not limited to MR pulse sequences, machine learning