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: Doctor of Veterinary Medicine (DVM) or equivalent degree, and/or PhD in a related field, completed by the start date. Preferred Experience and Skills: Strong organizational and time-management skills
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methods, advanced statistical techniques, and other areas. Responsibilities of the successful candidate include but are not limited to: Leading experiments focused on tobacco and cannabis product emissions
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National Energy Technology Laboratory (NETL) | Pittsburgh, Pennsylvania | United States | about 12 hours ago
, the program administrator, or any other office or agency. Qualifications The ideal candidate would have some, but not necessarily all, knowledge of and skills in: PhD in Physics or Chemistry Experience with
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statistical analyses will be performed primarily using R. The research is carried out in a multidisciplinary environment and in collaboration with clinical researchers and other relevant partners. Work duties
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, in collaboration with faculty and research staff at NCEMS, will conduct original research applying machine learning, artificial intelligence, computer science, statistical learning, or related
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privacy for data to be used for machine and statistical learning. It is well known that data can be highly sensitive, and that naive anonymization is not sufficient to avoid disclosure. Models and
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employed as post-docs, i.e. obtaining a PhD degree in the year of employment in the project or in the period of 7 years prior to 1 January of the year of employment in the project and other documents
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statistical / machine learning methods, pathway/network analysis or artificial intelligence approaches. Other requirements: The candidate should have a PhD or equivalent degree in bioinformatics, artificial
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to teaching activities may be discussed, including the possibility of supervising Master’s students. Profile Applicants should have: • a PhD in physics or related fields, completed or expected to be completed
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validation, while establishing protocols for sensor calibration, spatial sampling, quality control and integration with Earth Observation data. Develop spatial, statistical and predictive models to investigate