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Field
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various sets of data to prepare databases for cross-disciplinary AI research and learning. Incorporates open knowledge graph networks. Takes the lead in drafting scientific papers and technical documents
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. Investigate and build robust data and AI agent pipelines for continuous learning and knowledge acquisition, including annotation strategies and knowledge graph development for aquaculture stress events. Design
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Jhunjhunwala Lab at Genentech, please go to: https://www.gene.com/scientists/our-scientists/suchit-jhunjhunwala Relevant publications: Thrift, W. J. et al. Graph-pMHC: graph neural network approach to MHC class
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, consolidating metadata and other various sets of data to prepare databases for cross-disciplinary AI research and learning. Incorporates open knowledge graph networks. Takes the lead in drafting scientific papers
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as required. Demonstrated high level of written and oral communication skills. Preferable Experience in eukaryotic cell culture/tissue culture Expertise with advanced graphing and/or data analysis
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workflows — including methods for knowledge graph construction, advanced querying, and data quality assurance. Contribute to aligning the developed methods with emerging standards for information modelling
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HiPerBreedSim project. In this role, you will leverage recent advances in working with ancestral recombination graphs (ARGs) to develop algorithms and code for simulating population genomic data, including
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. Research areas include Representation Learning, Machine learning and Optimization on graphs and manifolds, as well as applications of geometric methods in the Sciences. This is a one-year position with
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. Demonstrated high level of written and oral communication skills. Preferable Experience in eukaryotic cell culture/tissue culture Expertise with advanced graphing and/or data analysis software (Prism, Origin Pro
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regarding research results. Preferred Qualifications: Experience with deep/graph neural networks and active involvement in data science and machine learning projects. Experience in multimodal data fusion (e.g