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Artificial Intelligence, Machine Learning, Deep Learning, computer vision, medical image segmentation, Computer-aided diagnosis, Computer-aided detection, Medical Image Analysis, Object detection, classification, Transfer Learning, Clustering, Precision Medicine, Personalized Medicine, Electronic Health Records, Wearable Devices, Health Informatics, Radiation therapy, Radiology, lung cancer, organs at risk, pancreas, liver etc.
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The Servicing Intelligence data science team builds deep learning models to help our internal customers use unstructured, multi-modal data sources text, image, and audio data. At least 2 years of experience working with deep learning libraries and tools, such as Pytorch and HuggingFace.
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We work on a broad range of machine learning problems across NLP/NLU, image generation, and speech perception. As a Machine Learning Engineer at Ello, you’ll design and develop state-of-the-art machine learning systems for reading instruction, story generation, and interactive dialogue teaching.
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5+ years of industry or research experience in state-of-the-art techniques in Machine Learning, Deep Learning, Computer Vision and Image Processing. Knowledge of Deep learning frameworks such as PyTorch, TensorFlow or Keras.
$152,000 - $209,000 a yearExpandApply NowActive JobUpdated Yesterday - UpvoteDownvoteShare Job
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Two (2) years of experience with R&D of machine learning and deep learning algorithms. Candidate must have subject matter expertise in computer vision and machine/deep learning.
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Headquartered in the Netherlands and operating in more than 100 countries globally, we focus our advanced technology and deep clinical and consumer insights on Precision Diagnosis, Image Guided Therapy, Enterprise Informatics, Monitoring/ Connected Care, Sleep & Respiratory Care and Personal Health.
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Oversee the continued development of machine learning including supervised machine learning, unsupervised machine learning, deep learning, optimization solutions, image/visual analytics, and natural language processing.
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We seek to advance the fields of computer vision and deep learning through research and development and through collaborative projects that build on our open source software platform, such as the Kitware Image and Video Exploitation and Retrieval (KWIVER) toolkit.
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CUDA, Julia, R and/or SQL), machine learning / deep learning methods including neural network design, computer vision / image analysis, and statistical analysis. Preference will be given to applicants with knowledge and experience in medical image analysis, including computed tomography, ultrasound, and x-ray radiography / fluoroscopy.
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We’re always striving for peak GPU efficiency on current and future-generation GPUs. To get a sense of the code we write, check out our CUTLASS open-source project showcasing performant matrix multiply on NVIDIA’s Tensor Cores with CUDA. This specific position primarily deals with code lower in the deep learning software stack, right down to the GPU HW.What you'll be doing:Writing highly tuned compute kernels, mostly in C.
$176,000 - $333,500 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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We are looking for inquisitive, creative applied machine learning scientists with expertise in deep learning algorithms for image or time-series data. Experience with one or more Deep Learning packages including but not limited to TensorFlow and PyTorch.
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Build scalable and effective systems for serving deep learning models - Build and optimize distributed training approaches for deep learning models on ML accelerators such as GPUs - Build software that improves the rate of experimentation and helps people make better decisions about what to try next - Train, evaluate, and debug deep learning models for complex tasks - Develop tools and services for understanding the latest NLP/Image models.
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Sr. Computer Vision and Machine Learning Engineer (hybrid) We have an immediate opening for a Sr. computer vision and machine learning engineer with experience in image dataset creation, object detection, deep learning, and AI modeling.
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Caper builds smart shopping carts powered by deep learning and computer vision to enable a seamless grab-and-go retail experience. Caper’s autonomous checkout technology is plug and play, meaning it requires no in-store renovation, no operational overhaul, no heavy computations, or endless image labeling.
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The candidate will apply machine learning and deep learning techniques to solve challenges in today's image-guided radiation therapy, including real-time low-contrast liver tumor localization, automatic structure segmentation for on-line adaptive radiotherapy, and real-time MRI motion estimation and reconstruction.
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