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Support applications of Artificial Intelligence (AI) in various research disciplines and serve as the domain expert. Experience with one or more machine learning and deep learning frameworks such as TensorFlow, PyTorch, or Keras.
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Required: Master of Science (with 5 years of experience) or entry-level Doctorate in the computational and data sciences including statistics, artificial intelligence, biostatistics, bioinformatics, and statistical genetics.
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Image Processing, Computational Imaging, Computer Vision; Practical experience with Deep Learning, Machine Learning, or Artificial Intelligence, for images, is preferred; Capable of prototyping Algorithms using MATLAB or Python, and implementing algorithms in C.
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Requirements EDUCATION Bachelor’s degree in Math, Science, Statistics, Economics, Finance, Informatics, Computer Science, Information Systems, Health Information, Epidemiology, Data Analytics, Data Science, Predictive Analytics, Artificial Intelligence, Engineering, or Physics required.
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Develop and adapt Large Language Models (LLM) and Natural Language Processing (NLP) methodologies using artificial intelligence and machine learning (AI/ML) frameworks and programming languages to create new capabilities to improve analytic workflows and address key intelligence questions.
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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 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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Your primary responsibility will be to propose, lead, and contribute to projects using artificial intelligence, deep learning, and machine learning technologies to support of APL's internal research efforts and sponsored programs.
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What you would do as a Machine Learning Engineer: Reporting to the Commercial Development Manager, you would work in parallel with the Openings Studio software engineers, Openings Studio Product Owner and divisions to identify technical opportunities for growth in future-forward areas (e.g. machine learning, artificial intelligence, etc.
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Possess direct experience and stay at the forefront of latest trends in machine learning, statistical test design, media mix modeling, artificial intelligence, and other data science fields.
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The Data Scientist will conduct artificial intelligence and machine learning (AI/ML) research and development for a portfolio of NASA applications with focus on aeronautics projects such as Air Traffic Management, Aviation Safety, Sustainable Aviation, and/or Advanced Air Mobility.
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Conduct feasibility study and gap analysis to implement artificial intelligence features to the existing software. Net, C#, Android SDK, Kotlin, IOS Swift, AWS: EMR, S3, Redshift, Athena, Glue, DynamoDB, Lamda, CloudWatch, ElastiCache, Kinesis, SNS, SQS, Azure: HDInsight, Data Factory, Data Lake Analytics, Databricks, GCP, and Big Data Apache: Hadoop, Hive, Hue, Sqoop, Kafka, Spark, Airflow, SnowFlake, Python, Scala, Machine Learning: NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Apache Mahout, Vertex AI, Keras, OpenNN, Theano.
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At the heart of Empower's pioneering journey, the Vice President of Data Analytics & AI stands as a beacon of innovation, steering our mission into uncharted territories with the power of data and artificial intelligence.
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Do you enjoy applying artificial intelligence and machine learning to solve real-world problems? Are experienced with one or more deep learning frameworks (e.g., PyTorch, Tensorflow) and with designing ML architectures for image/video analysis tasks (e.g., object detection, classification, and tracking.
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Have experience working with Linux-based operating systems, DevOps tools such as version control and CI/CD, containerization tools (e.g., Docker, Kubernetes), or tools for deploying production ML models (e.g., NVIDIA Triton, Amazon Sagemaker, Kubeflow.
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The Sponsor seeks to develop and adapt Large Language Models (LLM) and Natural Language Processing (NLP) methodologies using artificial intelligence and machine learning (AI/ML) frameworks and programming languages to create new capabilities to improve analytic workflows and address key intelligence questions.
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