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Field
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learning models for real-time image and video analysis (e.g., segmentation, object tracking, reinforcement learning), with applications to medical imaging and robotic systems. In this role, you will
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methods of data analytics (e.g., statistics, stochastic analysis, Bayesian statistical analysis), physically-based hydrology and water quality models, and the use of machine learning tools for modeling flow
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the quantification of uncertainty for these models, e.g. through Bayesian techniques, sensitivity analysis or similar. You also possess: A PhD in Hydrology, Earth Sciences, Water Management or a similar
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variants on human traits and single-cell readouts. Our research group is pioneering computational methods for deciphering molecular variation across individuals, space, and time. We have a track record in
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to offer. Qualifications: Required: PhD in ecology by start date Experience in plant phenology, biogeography, and spatial and temporal modeling (Bayesian and frequentist) Expertise in R or Python, GIS, big
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Translating deep learning models into clinical settings Experience developing deep learning models for real-time image/video segmentation, object tracking, 3D reconstruction, super-resolution. Have a passion on
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of using Bayesian methods in both model development and fitting. Previous experience and knowledge of research methods and study design in clinical trials. Knowledge of Good Clinical Practice (GCP) in
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/Seurat, count models, batch correction, differential analyses). Strong grounding in statistics (GLMs, hierarchical/Bayesian modeling, multiple testing) and experimental-design principles. Bioinformatics
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links) and Space Domain Awareness (tracking satellites, debris, and near-Earth objects). Expanding AO to these domains introduces challenges: extreme performance for faint or high-contrast targets
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organoids Experience with cerebral organoid models (is preferred but not mandatory) Molecular and biological assays to characterize stem cells and extracellular vesicles ES4TERM group has long track record in