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Have experience applying signal processing, image processing, pattern recognition, machine learning, deep learning, or related techniques with respect to signal extraction from sensors.
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Our group's diverse expertise enables us to solve a wide range of radar, electronic warfare (EW), remote sensing, and signal processing problems for maritime missions. Have 3 or more years of experience applying signal processing, pattern recognition, machine learning, deep learning, or related techniques with respect to information extraction from electromagnetic systems.
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We're looking for a Machine Learning Engineer who will build out and design machine learning infrastructure for a team of data & remote sensing scientists, working collaboratively with the Analytics and Software teams to deliver data-driven products and solutions.
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A successful candidate will have working knowledge in one or more of the following areas: advanced signal processing, radar systems, RF remote sensing, or resource management; or a strong background in a foundational field for these areas (i.e. - mathematics, physics, etc.
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Required: Experience in one or more of the following:digital signal processing (DSP),radar measurements and radar signal processing,remote sensing (synthetic aperture radar (SAR), hyperspectral imaging (HSI), etc.
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This project will provide preserve managers at the JLDP and beyond with accurate and actionable information to enable coordinated scaling of iceplant removal and habitat restoration while advancing our understanding of how to scale machine learning applications to Earth observation across space and time.
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Exemplify a commitment to excellence in research, operations, and community engagement, and work cooperatively to leverage scientific capabilities across ORNL. Work in a highly collaborative environment with data scientists, machine learning scientists, remote sensing scientists, HPC engineers, and geographers to deliver research and development prototypes.
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Experience working within the GEOINT, Earth Imagery, Derivatives, and/or Artificial Intelligence / Machine Learning markets. 10+ years of enterprise experience in relevant technical and business fields in remote sensing, defense and intelligence or the geospatial industry.
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Ideal candidates will have a background in fields related to Earth observation and terrestrial ecology, with demonstrated interests in remote sensing science, machine learning, ecosystem structure and biomass, ecosystem modeling, and studies on habitat/diversity, among others.
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6+ years of work experience, preferably in the autonomous vehicle, robotics, or remote sensing fields. Bachelor's Degree, or higher-level degree, in Computer Science, Engineering, or related quantitative field that demonstrates knowledge of AI, machine learning, optimization, random processes, sensor signal processing, or mathematical modeling.
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Develop software to prototype and evaluate signal processing, remote sensing, and/or machine learning algorithms. Basic knowledge of one or more of the following: signal processing, image processing, geospatial data processing, machine learning, numerical analysis, statistics, robotics, or distributed computing.
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Hydroinformatics - Motivated by the expansion of data acquisition systems and the need for managing and using massive datasets, this position requires specialization in information systems and technology for water management, engineering, and operations, with expertise in extreme data management, analytics, data mining, large dataset access, remote sensing, artificial intelligence, machine learning, water systems modeling, and geographic information systems.
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SciTec has an immediate opportunity for a talented Algorithm Technical Lead to support programs focusing on Image and Digital Signal Processing, State Estimation, and Classification for exploitation of data from remote sensing systems (ground-based, airborne and space-based.
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Working with a faculty adviser, the postdoctoral fellow will conduct specialized research in one or more areas of Atmospheric Science, including atmospheric chemistry; climate and atmosphere-ocean dynamics; cloud microphysics, severe storms, and mesoscale meteorology; data assimilation, machine learning, and causal discovery; education and human resources; global biogeochemical cycles and ecosystems; and radiation and remote sensing.
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The department houses a Bachelor’s degree in Geography with an optional GIS emphasis, a minor in GIS, and a certificate in GIS. We prefer candidates with expertise in remote sensing, field data collection, spatial data science, Earth data analytics, and/or machine learning.
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