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research directions include: Reversible material representation methods for accelerated inverse design Large language, diffusion & graph neural models for materials discovery Fine tuning and architecture
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hypotheses. This is a fantastic opportunity to contribute to world-class science in a leading biotechnology company. Who you are: Candidates must have a PhD in Computational Biology or Computational Science
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, acknowledging receipts of proposals, and maintaining a system to track proposals. Evaluate and perform preliminary analysis of the data using graphs, charts or tables to highlight the key points of the research
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-weight spatio-temporal graphs for segmentation and ejection fraction prediction in cardiac ultrasound, MICCAI, 2023 [3]Trosten et al., Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few
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-weight spatio-temporal graphs for segmentation and ejection fraction prediction in cardiac ultrasound, MICCAI, 2023 [3]Trosten et al., Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few
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representation methods for accelerated inverse design Large language, diffusion & graph neural models for materials discovery Fine tuning and architecture optimisation of foundation models Inverse design of next
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PhD in Computer Science, Engineering or other Machine Learning-related field. • Programming experience in python, C++ or other relevant language and experience in deep neural networks • Strong
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, and clinical safety datasets Implement graph-based retrieval-augmented generation (RAG) methods to enhance knowledge extraction and information synthesis Develop cross-pathway analytical methods using
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knowledge graphs and multimodal embeddings for cancer patient digital twin construction. Lead and co-author high-impact publications and grant proposals. Collaborate with clinicians, bioinformaticians, and