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research, especially deep learning literature, and how these methods apply to our use cases Ability to manage multiple projects and assignments with a high level of autonomy and accountability for results
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: A PhD in Physics, Computer Science, Mathematics, Machine Learning or relevant fields. Strong publication record in top conferences/journals, such as Nature Physics, Nature Communications, PRL, T-PAMI
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generalization across tasks. Hierarchical and modular agent architectures to enable scalable coordination. Design and implement simulation environments, integrating real-world data and domain-specific constraints
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bonding strategies to enable mechanical durability and signal fidelity in multimodal sensors. Conduct validation of sensor arrays across multiple modalities, ensuring performance across variable
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and MALDI/ESI, and lignin composition using TDA/GCMS. The postdoc will work closely with teams developing engineered plants to develop a deeper understanding of cell wall architecture. This is expected
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, the development and fine-tuning of vision foundation models, multiple instance learning, survival analysis, and interpretable model development. You will also lead efforts in building multimodal deep learning
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, the development and fine-tuning of vision foundation models, multiple instance learning, survival analysis, and interpretable model development. You will also lead efforts in building multimodal deep learning
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Qualifications Research experience with unsupervised and weakly supervised CNN and RNN architectures such as GANs, contrastive Learning, multiple instance learning, and transformer models Experience with