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is an interdisciplinary effort at the frontier between Biology (Genetics, Genomics), Bioinformatics, Artificial Intelligence (Neural Networks) and Statistics (LMMs). The aim is to join the
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techniques and models: convolutional neural networks (CNNs), vision transformers (ViTs), multimodal CLIP models, contrastive learning, natural language processing (NLP), attention mechanisms, variational
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informed machine learning methods and uninformed approaches and analysis of model explainability. - Machine and deep learning techniques and models: convolutional neural networks (CNNs), vision
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skills in Python, Java, etc. ● Expertise in machine learning, neural networks, and deep learning ● Excellent writing and communication skills ● Highly motivated, ability to identify potential problems and
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or assembloids to model brain development, neural circuit formation, or disease pathophysiology. A major emphasis will be on the development of scalable imaging pipelines that leverage artificial intelligence
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as an innovative and dynamic institution. We were one of the first higher education institutions to hire a Chief Artificial Intelligence Officer (https://www.gmu.edu/AI). Departmentally, we have
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. Recent advances in Deep Learning [LeCun2005] make it possible to study approaches based on neural networks to solve complex problems. These networks are resource intensive, often making them difficult
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at the intersection of artificial intelligence and cultural heritage. The successful candidate will be involved in cutting-edge research and development in 3D computer vision and machine learning for the digital
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neural networks inspired by the human brain and examine what mechanisms enable the networks to acquire human-like intelligence. For more information, please visit our lab homepage. We are currently seeking
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Infrastructure? No Offer Description Electroencephalography (EEG) and magnetic resonance imaging (MRI) can detect early alterations in neural networks that manifest as cognitive and sleep-wake cycle disorders in