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Emerging Technologies Group: Covers a wide range of work including Internet of Things (IoT) penetration testing, intelligent automation, artificial intelligence/machine learning and quantum computing.
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Design and implement deep-learning (DL) and machine-learning (ML) models to extract valuable insights from large repositories of time-series/biosensor data. This position is with the Signal Processing team at WHOOP. As a Signal Processing Engineer, focused on Deep Learning, you will be part of a cross-functional team composed of Signal Processing, WHOOP Labs, Firmware, and Data Science.
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A cornerstone of this roadmap is the acceleration of its data transformation and of the adoption of artificial intelligence (AI) and machine learning (ML) solutions. A cornerstone of this roadmap is the acceleration of its data organization and adoption of artificial intelligence (AI) and machine learning (ML) solutions, to accelerate R&D, manufacturing and commercial performance and bring better drugs and vaccines to patients faster, to improve health and save lives.
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We are specifically looking for individuals with PhDs in NLP, Machine Learning or Computational Linguistics, who can combine a research background with more recent hands-on Engineering abilities.
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5+ years in classical computer vision techniques, machine learning methods and tools including deep learning, associated frameworks (e.g., PyTorch, TensorFlow, Keras), signal processing modalities (e.g., EO/IR, sonar, radar, lidar, acoustic, LDV), or related.
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For methods builders we also welcome links to github/bitbucket pages, and relevant bioinformatics/computational biology/machine learning community contributions. PhD in a relevant subject area (Computational Biology, Population Genetics/Genomics, Bioinformatics, Biostatistics.
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You will have the opportunity to apply your knowledge in computational biology, machine learning, computer science, and statistics to develop and improve algorithms for analysis of multidimensional biomedical data and provide creative solutions to cutting-edge problems in genomics and drug discovery.
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Contribute to the development and refinement of machine learning models, including deep learning models for computer vision and natural language processing, to enhance Cria's capabilities in neonatal medicine.
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San Francisco, California (Hybrid On-Site): $243,800 - $278,200 for Sr. Lead Machine Learning Engineer. New York City (Hybrid On-Site): $230,100 - $262,700 for Sr. Lead Machine Learning Engineer.
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Strong experience with Python/R, forecasting methodologies, machine learning and statistics, Xometry is an on-demand manufacturing marketplace that leverages machine learning (ML) techniques to predict the manufacturing cost of machined parts and produce automated buy it now prices on Xometry.com. Customers love our instant quoting engine, and that engine depends on Xometry's ML team and technology.
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Stay abreast of latest AI trends and maintain state-of-the-art knowledge in machine learning, deep learning, and computational linguistics. Experience with machine learning techniques and tools including deep learning, reinforcement learning, and unsupervised learning.
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Collaborate effectively with renowned GSK experts in genetics, genomics, computational bio & chem, structural biology and medicinal chemistry, artificial intelligence / machine learning, and digital technologies to build the datasets, tools, models, and infrastructure necessary for oligo design & development.
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Strong familiarity with topics of protein and macromolecular structures and interactions with other proteinsExperience with cloud environments such as AWSExperience in developing machine learning platforms using modern ML frameworks for deep learning (, tensorflow, keras, MXNet) & deploying in services such as Amazon Sagemaker.
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If you have a solid foundation in machine learning and a keen interest in peptide/protein engineering and design, you could be pivotal in our journey. Your foundation in machine learning, combined with an interest in peptide/protein design, is what we value.
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Expertise in scientific programming languages (e.g., Python, R) and machine learning libraries (e.g., PyTorch, Tensorflow) We seek an enthusiastic and collaborative machine learning scientist with a focus on AI/ML methods to join our Small Molecule Drug Discovery team.
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