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the eDIAMOND project, namely: Distributing model training and inference over a network of resource-constrained devices. Online, context-aware adaptation of Federated Neural Network Architectures based
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experimental methods. Develop and apply methods for demultiplexing, normalization/QC, effect-size estimation, biological inference, and predictive modeling. Contribute to biological manuscripts and methods
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experimental methods. Develop and apply methods for demultiplexing, normalization/QC, effect-size estimation, biological inference, and predictive modeling. Contribute to biological manuscripts and methods
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of forest dynamics as simulated compared to real-world patterns that may be inferred from large inventory datasets (e.g., EuFoRIa, cf. above) or remote sensing. For your work, you can rely on an extensive
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inference) to uncover complex biological functions. Collaborating with experimental biologists to validate models and translate insights into practical recommendations for sustainable farming. Contributing
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emerging methodologies (e.g., causal inference) to uncover complex biological functions. Collaborating with experimental biologists to validate models and translate insights into practical recommendations
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career as a principal investigator. Possess a strong track record of innovation in computer vision, AI, and/or causal inference, with a passion for applying these to human model systems. Exhibit
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from marine and terrestrial sources, through its chemical transformation in the atmosphere, and finally to its deposition as wet or aerosol particles. You will also use statistical inference methods
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the eDIAMOND project, namely: Distributing model training and inference over a network of resource-constrained devices. Online, context-aware adaptation of Federated Neural Network Architectures based
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emission from marine and terrestrial sources, through its chemical transformation in the atmosphere, and finally to its deposition as wet or aerosol particles. You will also use statistical inference methods