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learning with mechanistic models; foundation models of genome regulation using single-cell and spatial multi-omics data; AI-based modeling of protein structure and protein interaction networks; AI-based
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Group Leader and Professor AI in Biology - Dept of Computer Science and Dept. Electrical Engineering
spatial multi-omics data; AI-based modeling of protein structure and protein interaction networks; AI-based modeling of cell morphology and tissue function using imaging and computer vision; AI models
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for the precise design of quantum dots while retaining the exceptional mechanical properties of graphene. The electronic structure of bilayer graphene has been the subject of extensive study in recent years
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of chemical reactivity and selectivity; molecular and biomolecular structure, function, and design, including generative design of biological systems; chemical biology; and the discovery of functional molecules
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* Applications are accepted electronically ONLY at www.cu.edu/cu-careers * Reporting to the Director of New Student Orientation this position is responsible for assisting with the planning, implementation and
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | about 2 months ago
Chemistry I Course Description:Atomic structure; periodic properties of the elements; bonding theories-ionic, covalent (valence bond and molecular orbital) and metallic; structure and bonding in coordination
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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before the fellowship start date. Demonstrated experience with electronic-structure calculations (DFT, plane-wave codes such as VASP or Quantum ESPRESSO). Experience with molecular dynamics and/or
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candidate will join the Scientific Machine Learning group at TDB and SciLifeLab. The group develops theory, methods and software for data-driven science, with a current focus on uncertainty quantification