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As an Applied Research Scientist, you will be applying innovative data science and graph learning algorithms to generate insights from large-scale computer networks to solve cybersecurity issues faced by businesses of all scales.
$200,000 - $240,000 a yearFull-timeExpandApply NowActive JobUpdated 2 days ago - UpvoteDownvoteShare Job
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TELUS International AI-Data Solutions partners with a diverse and vibrant community to help our customers enhance their AI and machine learning models. Opening within a company with a solid track record of successA role that offers a breadth of learning opportunitiesOpportunity to make a positive impact.
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CMO applies deep learning based causal inference models to measure omni-channel effectiveness of marketing campaigns from these businesses worldwide. Such challenges include measuring the incremental impact of multi-channel marketing portfolios, developing Deep Learning models for estimating the impact on sparse customer actions, and scaling measurement solutions for WW marketplaces.
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From creating experiments and prototyping implementations to designing new architectures, our research scientists work on real-world problems that span the breadth of computer science, such as machine (and deep) learning, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more.
$136,000 - $200,000 a yearFull-timeExpandApply NowActive JobUpdated 2 days ago - UpvoteDownvoteShare Job
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PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field. - Experience with Deep Learning for search and recommendation systems.
$124,100 - $212,800 a yearFull-timeExpandApply NowActive JobUpdated 1 month ago - UpvoteDownvoteShare Job
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Experience in research policy, bioethics, or management of big data; or demonstrated interest in learning more. Are you interested in learning more about the policy side of human genetics research.
$108,800 - $163,200 a yearFull-timeExpandApply NowActive JobUpdated 5 days ago - UpvoteDownvoteShare Job
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Research Intern - Machine Learning Acceleration (PhD) Responsibilities. You will use your background in Machine Learning to enable efficient hardware acceleration of ML algorithms for Computer Vision and Image Processing in AR and VR devices.
$7,500 - $11,333 a yearFull-timeExpandApply NowActive JobUpdated 1 month ago - UpvoteDownvoteShare Job
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Experience with Deep Learning Frameworks (Pytorch, TF, JAX, etc.) Familiarity with linear algebra and supervised machine learning. Your role on the team will be to scale up learning algorithms on state-of-the-art hardware.
$105,000 - $250,000 a yearFull-timeExpandApply NowActive JobUpdated 1 month ago - UpvoteDownvoteShare Job
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Machine learning/deep learning video compression approaches. Video pre-/post-processing algorithms with deep learning and computer vision. and deep learning. Machine learning applied to computer vision problems.
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Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment. The Audio Research team is focused on harnessing the power of AR and VR to make virtual sounds indistinguishable from reality and to redefine hearing capabilities of billions of people, forever transforming the way we work, play, and connect.
$56.25 - $173,000 a yearFull-timeExpandApply NowActive JobUpdated 2 days ago - UpvoteDownvoteShare Job
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Communication of research agenda, progress and results over the course of the intern project. Knowledge of optics, signal processing, mathematical modeling and/or simulation expertise with respect to visual perception and/or systems design.
$7,313 - $11,333 a monthFull-timeExpandApply NowActive JobUpdated 2 days ago - UpvoteDownvoteShare Job
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Its core technology is backed by more than 3 years of cutting-edge research of its co-founders who are recognized leaders in the federated machine learning community. Solve research challenges such as statistical heterogeneity, and also consider the constraints such as system heterogeneity, security, privacy for federated learning or distributed training platform.
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