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Field
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computational biology and bioinformatics, with experience in: Applying deep-learning or AI-based methods for image denoising. analysis of whole-genome sequencing (WGS) or other large-scale sequencing data
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), and in vivo fiber photometry (TDT). We are particularly looking for a PhD-level systems neuroscientist with expertise in animal behavior tracking using deep learning algorithms and their causal link
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application of advanced deep learning models, with an emphasis on techniques such as knowledge distillation. The candidate will engage in research involving time-series analysis, including modeling, forecasting
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learning, or related quantitative field. • Proficiency with deep learning frameworks (e.g., PyTorch, TensorFlow, and JAX). • Experience in PDE/ODE modeling and numerical methods. • Strong interest in
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fellowships and NSF SBE postdoctoral awards. We especially welcome applicants with theoretical interest in child language development, strong computational and analytical skills (deep learning frameworks), and
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of California Academic Personnel policy (APM 210 1-d ). These values are embedded in our Principles of Community , which reflect our passion for critical inquiry, debate, discovery and innovation, and our deep
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Language Processing, and Electronic Medical Record (EMR) data mining. Prior experience in deep learning, biophysics or omics data analysis is essential. Preference will be given to applicants with prior training in
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
. Responsibilities for this position includes:* Developing deep-learning approaches for insight and analysis about mouse behavior from video * Machine Learning: use your expertise in statistics and machine learning
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 10 hours ago
. Required Qualifications, Competencies, and Experience Expertise in Retrieval-Augmented Generation (RAG), Natural Language Processing (NLP), deep learning frameworks. Proficiency in Python and frameworks
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sequence programming (e.g., IDEA/ICE) and contemporary image reconstruction techniques (e.g., compressed sensing, parallel imaging, model-based or deep learning reconstructions). Knowledge of radial data