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Develop and implement machine learning and deep learning models to enhance predictive analytics for tree disease detection. Your role will focus on utilizing machine learning and deep learning technologies to advance our platform, which integrates UAV and satellite remote sensing data for environmental monitoring.
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What Additional Experience Makes A Strong Candidate: 4+ years of production experience with Scala 4+ years of production experience with Micro Services / Streaming Services Experience addressing ML problems, particularly in domains such as Time Series, Computer Vision (CV), Natural Language Processing (NLP), and data mining Proficiency with popular ML frameworks (Xgboost, TensorFlow, PyTorch, etc.
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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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Develop the data analytics and AI/ML skills, capabilities, processes, and tools for Teva to apply advanced techniques to achieve its growth agenda, such as machine learning/artificial intelligence, generative AI, natural language processing, natural language generation, modeling and simulation, predictive analytics, etc.
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Examples include Azure Machine Learning Studio, Google’s Vertex AI, IBM Watson Studio, Amazon SageMaker and open-source tools like Kubeflow. SG Analytics (SGA), a Global Insights and Analytics company, focuses on ESG, Data Analytics, and Investment & Market research services.
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The Data Science team at Meijer leads the strategy, development and integration of Machine Learning and Artificial Intelligence at Meijer. Build production ready prototypes for, and iteratively develop, end-to-end data science pipelines including custom algorithms, statistical models, machine learning and artificial intelligence functions to meet end user needs.
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Java, Python, ScalaExperience in Natural Language Understanding, Computer Vision, Machine Learning, Algorithmic Foundations of Optimization, Data Mining or Machine Intelligence (Artificial Intelligence.
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Participate in data mining for marketing insights and implementation of predictive analytics and member next best action decisioning to meet Navy Federal product, revenue, and member engagement goals.
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A rapidly expanding AI-powered customer analytics company focused on delivering deep customer insights and personalized experiences using cutting-edge artificial intelligence, machine learning, and data science techniques.
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Machine learning will cover everything from data science, Artificial intelligence, business analytics, deep learning, and computer science. In our Data science track we prepare you to get job as one of the following: Python developer, a data analyst, data visualization developer, a statistician, a machine learning engineer or a data scientist.
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The qualified candidate for this position will lead TSL Developmental Test & Evaluation (DT&E) projects, Internal Research and Development (IRaD) projects, and other company divisions with machine learning and artificial intelligence expertise.
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Strong background in computer science, mathematics, or related fields, with expertise in machine learning, deep learning, computer vision, natural language processing, and understanding of world model architectures.
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We are a growing subsidiary of a large public company that is hiring a talented Lead ML Engineer / Senior Machine Learning Engineer! 100% REMOTE Senior ML Engineer / Lead Machine Learning Engineer Needed for Growing Subsidiary of a Large Public Company.
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Ideal candidates have a strong publication record in areas including artificial intelligence data mining, machine learning, natural language processing, and/or fields such as cognitive science, learning analytics, and psychometrics.
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Basic knowledge and experience in statistical and data mining techniques such as: generalized linear model (GLM)/regression, random forest, boosting, trees, text mining, hierarchical clustering, deep learning, convolutional neural network (CNN), recurrent neural network (RNN), T-distributed Stochastic Neighbor Embedding (t-SNE), graph analysis, etc.
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