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. Description: NASA Inexpensive Network Sensor Technology for Exploring Pollution (INSTEP) is a low-cost air quality sensor network utilized in combination with NASA remote sensing datasets to analyze air quality
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Environmental Research and Development Program (SERDP) and the Environmental Security Technology Certification Program (ESTCP) is offering a postdoctoral fellowship. Why should I apply and what will I be doing
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facilities conducting research in DHS relevant areas? If you answered “Yes”, to the above questions, the HS-POWER program is for you! The U.S. Department of Homeland Security (DHS) Science and Technology
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, data engineering, data analytics, artificial intelligence, machine learning, deep learning, natural language processing, and automation using modern tools and techniques. During this fellowship, you will
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selected reactive dyes. Dye uptake is a critical aspect of textile coloration because it affects color uniformity, shade consistency, and the appearance of defects such as barré—key factors in product
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, horticulture, bioinformatics, computational biology, or a related field. A doctoral degree in one of these areas is recommended for participation. It would be favorable for a candidate to have experience in
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to rapidly detect and assess known, novel, and unforeseen health threats and help translate emerging signals into timely public health action. It connects surveillance systems, scientific expertise
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of Defense . Qualifications The ideal candidate will be a graduate student or received a master's or doctoral degree in Civil or Environmental Engineering, or Hydroligic Sciences. Preferred skills include
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experienced in their research fields but may not have the technical, information technology (IT), or data science experience to build digital tools that optimize research efficiency and maximize the impact of
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scales, from the genome to the continent, and sub-daily to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including AI and machine learning