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Demonstrated professional or academic experience with deep learning frameworks such as PyTorch or Tensorflow to optimize convolutional neural networks (CNN) such as ResNet or U-Net for object detection or segmentation tasks using satellite imagery.
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Machine learning will cover everything from datascience, Artificial intelligence, business analytics, deep learning, and computer science. Difference between Machine learning, Artificial Intelligence and Deep learning.
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We are looking for a Senior Data Scientist to build and deploy Natural Language processing (NLP) models utilizing a variety of Machine learning and deep learning techniques. The role will present opportunities to work on large datasets and the ability to use innovative techniques in Artificial Intelligence ranging from various NLP methods, computer vision, and deep learning to enable solutions that will be directly impactful to our customers.
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Experience with deep learning workloads, including performance tuning considerations such as parallelization and fusion strategies; As a member of our deep learning architecture team, you will contribute to features that help next-generation GPUs advance the state of AI.
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Hands-on experience of machine learning methodologies including advanced analytics tools (such as R and Python) along with applied mathematics, ML and Deep Learning frameworks and libraries (TensorFlow, PyTorch, Keras) and ML techniques.
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Familiarity with deep learning applied to a relevant domain like NLP or representation learning, e.g. RNNs, LSTMs, Transformers and associated libraries - tensorflow, keras, or pytorch.
$94,400 - $120,000 a yearFull-timeExpandUpdated 20 days ago - UpvoteDownvoteShare Job
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Strong coding skills in Python, and experience with machine-learning and deep learning libraries (e.g., Scikit-learn, TensorFlow, PyTorch, etc.) + Prior experience with applying machine learning and/or deep learning tools for medical and/or cellular imaging tasks is preferred.
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Computational tools and machine learning/deep learning techniques to analyze and interpret complex biological data relevant to drug discovery. in Artificial Intelligence/Machine Learning (AI/ML.
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Experience with deep-learning software (Pytorch and TensorFlow) and HPC analysis pipelines are required. The incumbent will have access to a comprehensive set of computing resources including ground breaking deep-learning workstations specifically dedicated to the Al-Louzi/Sati labs as well as the high-performance-computing (HPC) infrastructure supported by our institution.
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Dr. Terrance Boult and his team at the El Pomar Institute for Innovation and Commericalization (EPIIC) at the University of Colorado Colorado Springs (UCCS) are seeking one or more highly motivated advanced post-doctoral researcher(s) with a strong record of accomplishment in machine learning and computer vision, ideally with incremental learning, deep learning, or open-set learning, and/or with biometrics and computational photography.
$62,000 - $85,000 a yearExpandApply NowActive JobUpdated 2 days ago - UpvoteDownvoteShare Job
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You have experience with deep learning library like TensorFlow and/or PyTorch as well as experience with Spark and/or Hadoop. Have strong knowledge of Statistics, Machine Learning, Deep Learning and NLP/NLU.
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Five or more years of computer vision, machine learning, or biometrics research and development experience. Three or more years of computer vision, machine learning, or biometrics research and development experience.
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Knowledgeable in statistical and machine learning methods (e.g., NLP, deep learning, predictive modeling) Build a robust machine learning platform and infrastructure to enable rapid model design, implementation, validation, calibration, documentation, feature engineering, deployment, batch and real-time inference, and ongoing monitoring.
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Cursos especializados en deep learning, procesamiento de Imágenes, ingeniería de video, análisis de datos, multimedia y desarrollo de Aplicaciones de streaming. Despliegue optimizado de modelos de deep learning, monitorización remota de dispositivos en el edge, optimización de modelos con técnicas avanzadas y uso de NPUs en cámaras de videovigilancia.
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Experience using a cloud computing service to reduce runtime to train and evaluate deep learning models. Hands-on experience implementing, deploying, evaluating, fine-tuning and hyperparameter-tuning deep learning models at scale.
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