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Build and lead the data science team translating small molecule and proteomic drug discovery challenges into breakthroughs via deep learning applied to computational chemistry. This position is ideal for someone with deep expertise in machine learning, deep learning (particularly for building biologically meaningful representations of chemical structures), and computational chemistry, sciences and who is committed to building a high-preforming team.
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Learning: Learning & Development Opportunities to grow your skills and career. At Phoenix Tailings, we have an open culture that values learning, and we are looking to grow the team with enthusiastic individuals who share our vision of sustainable mining.
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Basic to substantial experience in one or more of the following commercial/open-source data discovery/analysis platforms: RStudio, Spark, KNIME, RapidMiner, Alteryx, Dataiku, H2O, SAS Enterprise Miner (SAS EM) and/or SAS Visual Data Mining and Machine Learning, Microsoft AzureML, IBM Watson Studio or SPSS Modeler, Amazon SageMaker, Google Cloud ML, SAP Predictive Analytics.
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Engage in planning sessions in collaboration with other Educator-Advisors and STEAM technician/makerspace manager to prepare for learning experiences and reflect on learning (to inform lessons and curriculum planning.
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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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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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Rope access experience or an interest in learning rope access techniques (SPRAT, IRATA). Actively engages multiple perspectives when solving problems, seeks to learn from peers, and encourages reciprocal learning among team members.
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As a Machine Learning Engineer II with CodaMetrix you will help with translating proof-of-concept ideas to product grade solutions. The Machine Learning Engineer II reports to the Director of Architecture.
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We are seeking a highly motivated Machine Learning Engineer Co-Op from July 2024 to December 2024, focusing on Natural Language Processing (NLP) and Computer Vision applications. 2024 Fall Co-Op - Machine Learning Engineer (NLP & Vision.
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The Youth Workplace learning Coordinator will be a member of the city’s Youth Services Team, a newly established division within the Health and Human Services Department that promotes and fosters the development of resilient, prepared, and engaged Somerville teens.
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Employ advanced AI and machine learning models, including deep learning and predictive analytics, to identify novel materials and predict their performance within battery systems. PhD in Chemistry, Artificial Intelligence, Machine Learning, Data Science, Material Science, or a closely related field.
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It leverages technologies like robotic process automation (RPA), machine learning, natural language processing, and blockchain to help its clients enhance operational efficiency, improve customer experience, and drive business growth.
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We have a fast-growing team that's distributed around the world, with offices in San Francisco and New York City. Our team has years of experience building and operating business-critical machine learning systems at leading tech companies like Uber, Google, Meta, Airbnb, Lyft, and Twitter.
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Advances in bigchemical data, massive computing power, artificial intelligence, andmolecular dynamics simulation are changing the way we develop newdrugs.
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Familiarity with Docker, Git, ML development toolkits (e.g. PyTorch) Familiarity with training and adapting Machine Learning models, and familiarity with the Huggingface ecosystem and Transformer family of models.
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learning job Title: expert Company: Apple Inc in Woburn, MA
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Talent acquisition is a multi-stage process where candidates undergo various application steps before getting hired. The unfortunate reality is that it is a labor-intense system, with the hiring manager and recruiter often handling all of the work on their own. Ask any one of them, and you will hear about the overabundance of applications and the demanding task of filtering through them to find the best candidates. The quality of talent suffers under the weight of all that work on one person's hands. It's not easy, but as many companies are starting to realize, there is a better way. The future of talent acquisition lies in collaborative recruiting!