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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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COMPUTER/IT Staff Research Scientist, IBM Corporation, San Jose, CA: Perform fundamental research within area of Machine Learning and Computational biology with an expectation that said research will result in published papers, patents, external eminence, and ultimately delivery of generated assets back to product for sale.
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Our core technical competencies include system-level modeling and gap analysis, advanced sensing and signal processing, machine learning and artificial intelligence, computational modeling, hardware and software prototyping, model-based systems engineering, establishing measures of performance, and human data collection in laboratory and field environments.
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The Research Computing Center (RCC) seeks to hire an experienced Computational Scientist – Scientific–AI and Machine Learning to serve as a domain expert in supporting and advising faculty, post-docs, and graduate students on projects in a wide range of research domains.
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Extensive experience developing, implementing, and training novel and scalable machine learning architectures on large multi-modal datasets in either scientific research or product development settings.
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You will lead a team of researchers creating data-driven algorithms for ATG. The initiatives will include both short-term and long-term research in areas such as artificial intelligence, machine learning & data mining, natural language processing, big data systems, information and data visualization, and social & cognitive science.
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Our specialized data centers run computing-intensive applications powered by clean energy for: Cryptocurrency Mining Artificial Intelligence Deep Learning Machine Learning Scientific Computing Natural Language Processing Video Transcoding and more.
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Five or more years of industry experience in data science with a focus on data analytics, machine learning, and deep learning. Data Governance: Implement and uphold robust data governance policies and procedures to ensure the secure and ethical use of internal company data and imagery for machine learning initiatives while adhering to privacy regulations and industry best practices.
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As a Staff Data Scientist, you will utilize your expertise in data mining, predictive modeling, machine learning, and NLP techniques to drive strategic decisions for WCS. You will be at the forefront of implementing models and strategies using A/B testing or Design of Experiments (DOE), tracking business performance, and providing insightful visualizations to support and continuously improve WCS business strategy.
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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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The Machine Learning Engineer will be responsible for architectural design and planning, advanced data pipelines, model integration and optimization, scalability, performance and research and innovation supporting production AI systems.
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Assist in Problem Solving: Troubleshoot complex issues related to machine learning model development and data pipelines and develop innovative solutions. S&P is a leader in risk management solutions leveraging automation and AI/ML. This role is a unique opportunity for hands-on ML scientists and NLP/Gen AI/ LLM scientists to grow into the next step in their career journey and apply her or his technical expertise in NLP, deep learning, GenAI, and LLMs to drive business value for multiple stakeholders while conducting cutting-edge applied research around LLMs, Gen AI, and related areas.
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As part of the Data Analytics team, the Lead Data Scientist will work closely with the Data Engineering team and business functions to solve real-world oil and gas midstream problems using machine learning, data science algorithms and artificial intelligence.
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Experience in scientific data analysis, statistical modelling, algorithm development, data visualization, machine learning, linear and non-linear regression, data cleaning, data analysis, epidemiology, and data mining.
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Techniques: Statistical Analysis, Visualization, Optimization, Machine Learning, Big Data, Data Warehouse, NLP. Drive the adoption of data science-driven mechanisms and machine learning models to continuously evaluate and improve catalog data quality.
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machine learning data mining scientist processing research jobs Company: Aci Federal
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