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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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Velvetech constantly follows the Tech trends and actively cooperates with startups in such breakthrough areas as Machine Learning, the Internet of Things, Blockchain, FPGA, and AI. and exciting projects with new opportunities and learning.
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ACE is an academic achievement and retention program, which is administered through the Division of Physical and Biological Sciences at UCSC. Initially, ACE supports students enrolled in gateway mathematics and science courses through active learning problem solving sessions and peer mentoring.
$27.15 - $32.04 an hourFull-timeExpandUpdated 14 days ago - UpvoteDownvoteShare Job
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The Machine Learning Engineer II will be a member of the Learning and Active Perception (LEAP) group in AV’s MacCready Works division and develop a variety of innovative computer vision detection, classification, localization, and tracking solutions for defense and commercial applications.
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Key Responsibilities:- Design, develop, and deploy advanced machine learning models and algorithms, leveraging deep learning frameworks such as PyTorch and TensorFlow. We are actively seeking a Senior Machine Learning Engineer to contribute to innovative projects for a prestigious Global Financial Services organization.
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Experience with building and iterating on machine/deep learning models using common frameworks such as PyTorch, Tensorflow, or JAX. Demonstrated expertise in implementing and fine-tuning neural networks, including design choices such as activation functions, loss functions, and regularization techniques.
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Kensho is a Machine Learning (ML) and Natural Language Processing (NLP) company, centered around providing cutting-edge solutions to meet the challenges of some of the largest and most successful businesses and institutions.
$175,000 - $225,000 a yearExpandUpdated 5 days ago - UpvoteDownvoteShare Job
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Working in a highly collaborative and interdisciplinary environment with ambitious goals, you will partner with other computational and bench scientists to conceive, implement, and validate machine learning that automate the interpretation of our physics-based ReSOLVE calculations.
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At least 10 years of industrial/academic experience advancing the state-of-the-art machine learning-based research through demonstrable, verifiable technical results in the area of natural language processing, human language understanding, computational linguistics.
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We seek machine learning engineers with a passion for sports to implement, automate, and optimize the quantitative models that power our world-class sports intelligence platforms in baseball, basketball, cricket, football (American), hockey, soccer, and tennis.
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The company’s comprehensive content, resources and support include 150+ full eCourses in core and elective subjects, plus career and technical education and social-emotional learning; actionable learning analytics dashboards and blockchain-enabled micro-credentials; and instructional services teachers and advisors who provide personalized student support.
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The Mathematics and Computer Science Division at Argonne National Laboratory seeks self-motivated and independent Postdoctoral researcher to develop and apply state-of-the-art probabilistic machine learning techniques for the development of efficient and robust surrogate models for scientific machine learning applications.
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The candidate should have expertise in data modeling, SQL, Python, R, data warehousing, big data technologies, data pipeline construction, data quality, cleansing, version control, cloud services, machine learning, feature engineering, deep learning, and natural language processing.
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4+ years experience as an Machine Learning Engineer, Applied Scientist, or similar role. As a Senior Machine Learning Engineer, you will be responsible for developing ML solutions to increase the safety, efficiency and sustainability of the physical operations.
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Will use data-science tools and pedagogy research to extract insights from complex datasets, design and conduct experiments, and build and analyze statistical models and tools; and work closely with Residential Education team members and others across MIT to communicate results and drive innovation in data-driven learning analytics.
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