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, and other methods. Collects data using cameras, sensors, and data acquisition systems. Prototypes, troubleshoots, and fine-tunes machine learning models for outcome predictions relevant to surgery
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and great opportunity of interdisciplinary training in machine learning and functional genomics. The project combines cutting-edge computational approaches, especially state-of-the-art machine learning
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an opportunity for renewal to perform research using artificial intelligence (AI) and machine learning (ML) with a focus on large language models (LLMs) and foundation models (FMs) relevant to electric power
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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health
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Post-doctoral Researcher in Multimodal Foundation Models for Brain Cancer & Neuro-degenerative Disea
Qualifications PhD in machine learning, computer vision or a related field. Established expertise in deep learning methods applied to images analysis. Experiences with generative models, volumetric image
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implements machine and deep learning programs. Develops algorithms to deconvolve RNA-seq data and compare them to AI-based methods. Performs follow up validation efforts on cell lines. Minimum Qualifications
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About the Position We are recruiting a Postdoctoral Research Associate to join Tom Hartvigsen’s research group in UVA’s School of Data Science. The postdoc will work on developing machine learning
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, evolutionary biology, computer science, physics, applied mathematics, or engineering. Our research integrates mathematical modeling, machine learning, and quantitative experiments to understand and control
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team at the Laboratoire d'Informatique de Grenoble (LIG). GetAlp conducts research in NLP, machine learning, evaluation, and interpretability. The project will be supervised by Maxime Peyrard (CNRS
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ESSENCE: Efficient Self-Supervised Machine Learning for Adaptive Wireless Communication Systems This project investigates self-supervised learning (SSL) for wireless communication systems to improve