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, engineering, physics, biophysics, applied mathematics, computational biology or a related quantitative field Strong background in deep learning for image analysis / computer vision, ideally on microscopy time
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leader • Excellent written and oral communication skills Preferred Qualifications • Background in antisemitism studies • Experience with R, NLP and deep learning libraries • High performance computing
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this position, please contact Anu Khara, Manager, Learning Design and Innovation, anuradha.khara@utas.edu.au . Please visit https://www.utas.edu.au/jobs/applying for our guide to applying and details
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 9 days ago
position will include, but is not limited to, multimodal+embodied semantics, human-like language generation and Q&A/dialogue, and interpretable and generalizable deep learning. The duties of the postdoctoral
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team players and passionate on cutting edge computer vision and machine learning technologies, as well as possess deep understanding of machine learning technology and experience on turning machine
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Description The Deep Learning laboratory in the Division of Science, New York University Abu Dhabi, seeks to recruit a Junior Research Scientist to work on Deep Reinforcement Learning (DRL
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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics
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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics
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learning community where young people see aspects of their backgrounds and identities reflected around them, where they feel a deep sense of belonging, and where they discover and use their voices to full
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solutions that enhance ecological monitoring, improve resilience planning, and promote sustainable resource management. Development of a Detection Transformer through Attentive Deep Learning and Explainable