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Experience with machine learning related open source libraries including, but not limited to: Hadoop, Spark, SciKit-Learn, TensorFlow, etc. 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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The department’s research portfolio is thriving, with faculty actively engaged in research in a diverse range of fields such as bioengineering, communications and networking, computer engineering, controls and robotics, cyber-physical systems, cyber security, digital forensics, machine learning, nanoelectronics, power systems, signal processing, and space-based systems.
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Argonne’s Leadership Computing Facility (ALCF) and Mathematics and Computer Science Division (MCS) is looking for a Postdoctoral appointee working at the intersection of scientific machine learning SciML and large-scale simulation codes.
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Expertise with Google Cloud Platform’s distributed Machine Learning, Data Science and Data Engineering tools (e.g. BigQuery ML, VertexAI, AutoML, Docker, Kubernetes, Kubeflow, Dataproc.
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Python, Java or Perl- PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field, or Master's degree- 5+ years’ experience with modeling languages and tools like PyTorch / TensorFlow, R, scikit-learn, numpy, scipy, etc.
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Desired Background PhD in Computer Science or related field 4-8 years of professional experience with relevant experience building and designing distributed data systems Knowledge of statistics, data science, AI/machine learning, big data management Strong proficiency in Python and an additional language (e.g., C.
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Much of our work contributes to innovative research in the fields of sensor science, signal processing, data fusion, artificial intelligence (AI), machine learning (ML), and augmented reality (AR.
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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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Education: Master's or Bachelor's degree in quantitative field such as engineering or mathematics (e.g. Electrical Engineering, Computer Engineering, Computer Science, Information Technology, or Information Systems); and shall possess Agile Developer certification or Certified Scrum Developer certification.
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You will deploy and deliver technical solutions at the intersection of computational chemistry and machine learning, supporting research directions in molecular design across broader gRED and Roche.
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Master's Degree in computer science, machine learning, applied mathematics, econometrics, statistics, engineering, physics, or related discipline preferred. Bachelor's Degree in computer science, machine learning, applied mathematics, econometrics, statistics, engineering, physics, or related discipline required.
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Proven experience with enterprise-scale technical delivery experience with AI Services including OpenAI, Schematic Kernel, AWS Bedrock, Machine Learning (or equivalent), Generative AI, LLM customization, NLP, Search, MLOps, Open-source AI frameworks, AI Infrastructure, architecture design.
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Explore new machine learning and artificial intelligence approaches for automated object detection, feature extraction/target state estimation, multi-source data fusion, and much more.
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Utilizing NVDA (NVIDIA) technologies such as CUDA, TensorRT, and GPU acceleration to optimize machine learning models and enhance the overall performance of applications. Additionally, expertise in NVDA (NVIDIA) technologies such as CUDA, TensorRT, and GPU acceleration will be crucial in optimizing our machine learning models and enhancing the overall performance of our applications.
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Employ machine learning/artificial intelligence (ML/AI) and neural networks to refine physics-based models for mechanical properties and material characterization. Mechanical Analysis Engineer with Machine Learning.
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