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Field
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techniques including deep neural networks and large language models (such as GPTs), with state-of-the-art functional genomics approaches, including crop genomics, genome editing, and single cell multiomics
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models, programming, and quantitative methods. Preferred qualifications include experience in reinforcement learning, neural networks, and/or statistics. Questions can be addressed to Professor Nathaniel
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genomics, virtual cell models Graph-based neural networks, optimal transport Biomedical imaging, deep learning, virtual reality, AI-driven image analysis Agentic systems, large language models Generative AI
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of single-cell data, and gene regulatory network mapping during neural development. Qualification requirements Applicants must hold a degree equivalent to a Norwegian doctoral degree in Cell Biology
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project is to develop a series of surrogate models focusing notably on Physics-Informed Neural Networks to emulate the process of sediment deposition, diagenesis, and potentially fracturing, working closely
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research questions. This postdoctoral scholarship offers the opportunity to be a part of this AI revolution by developing novel neural network architectures specifically optimized for plant genomic data. Our
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work on a study funded by the National Institute of Mental Health. The major goal of the project is to use a combination of modern circuit neuroscience technologies to establish neural circuits
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methods (e.g., PCA, PLS-DA, clustering, neural networks) to enable automated, polymer-specific classification. Optimize workflows for high-throughput imaging and real-world sample variability, minimizing
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informed neural networks (PINN) and explainable machine learning (EML) frameworks; experience in related technologies including large-scale data analysis, deep learning, Python, PyTorch; and the ability
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the goal of improving human health. Aligned with Rutgers University–New Brunswick and collaborating university wide, RBHS includes eight schools, a behavioral health network, and five centers and institutes