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them in this task. In this course, we will introduce the foundation concepts of data visualization such as proper use of different graphs. The course will cover the complete life-cycle of data
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position focuses on advancing the integration of gene regulatory network (GRN) simulations into multicellular and tissue-level systems using machine learning—particularly graph neural networks (GNNs) and
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on advancing the integration of gene regulatory network (GRN) simulations into multicellular and tissue-level systems using machine learning—particularly graph neural networks (GNNs) and reinforcement learning
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and the day-to-day administration of the MSc and PhD graduate student programs. Responsible for all graduate records and student files and related inquiries. This position is the liaison between
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diffusion models) for de novo molecular design. Proven experience in cheminformatics pipelines using tools such as RDKit, various molecular representation and encoding methods (SMILES, SELFIES, graph-based
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diffusion models) for de novo molecular design. Proven experience in cheminformatics pipelines using tools such as RDKit, various molecular representation and encoding methods (SMILES, SELFIES, graph-based
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, graph-based models), QSAR, virtual screening, or molecular docking/simulations. Knowledge in quantum chemistry calculations and molecule dynamics. Additional expertise that is desired (but not required
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are available immediately and intended for early-career scientists with an advanced degree (PhD). The selected fellow(s) will join the research group of a Principal Investigator through a TQT-funded project, and