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Natural History. The researcher will develop deep learning models to predict individual bee age based on wing morphology. This model will be trained of existing wing images and applied to images of museum
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electrocatalytic processes. This postdoctoral position will investigate pulse-mediated electrodeposition of metal particles using deep eutectic and organic solvents, ultimately aiming to induce kinked high-index
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, tissue sections, RNA/DNA, tabular data) for predictive modelling using software such as Python Documented experience of neural networks, image processing, deep learning algorithms, and data visualization
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. The Regenerative Immunology lab is currently composed of three PhD students, three postdoctoral fellows, one MS student, and one animal technician. The lab resides within the Division of Molecular Medicine and Gene
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. Significant experience of developing deep learning methods using computational frameworks such as PyTorch, TensorFlow etc. Experience of working with molecular questions in the biosciences and applying AI
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/bayesian/deep-learning analyses, with functional validation in spruce via CRISPR-Cas9 and nanoparticle delivery. The postdoc will join Professor Nathaniel R. Street’s team at UPSC, working closely with
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, Micro-C/Hi-C, BS-Seq/EM-Seq), massively parallel enhancer assays (ATAC-STARR-seq), and comparative/bayesian/deep-learning analyses, with functional validation in spruce via CRISPR-Cas9 and nanoparticle