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At the Leibniz Institute of Plant Biochemistry in the Department of Bioorganic Chemistry a position is available for a PhD in Machine Learning for Enzyme Design (m/f/d) (Salary group E13 TV-L, part
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to the development of state-of-the-art AI approaches applied to land surface monitoring, particularly using satellite observations. These approaches may include machine learning and deep learning methods
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the mobility of IoT devices. This thesis proposes leveraging intelligent softwarization—using Machine Learning (ML), Software-Defined Networking (SDN), and Network Function Virtualization (NFV
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biology platform https://www.scilifelab.se/units/structural-proteomics/ The unit provides access to cutting-edge equipment and expertise, for the analysis of protein interactions and conformational dynamics
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. Ability to learn and utilize computer software designed for law enforcement. High level of attention to detail. Good communication and interpersonal skills. Ability to use good judgement and make effective
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of engineering and computer sciences. There will be weekly, monthly, semesterly, and annual responsibilities to ensure the proper functioning of teaching spaces within the college across multiple buildings
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. Additional guidance is provided by graphic designers in M&C. Career Readiness Competencies: Take initiative to learn new tools, systems, or procedures on the job. Actively listen and respond respectfully in
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disability and need assistance with the hiring process, please call Human Resources at (870) 338-6474, ext. 1271. Department: Workforce Training Department's Website: Summary of Job Duties: Teach, develop and
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http://www.syracuse.edu. About the Syracuse area: Syracuse is a medium-sized city situated in the geographic center of New York State approximately 250 miles northwest of New York City. The metro-area
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YOUNG RESEARCHER IN THE FIELD OF EARLY DETECTION OF THE HEALTH STATUS OF PLANTS USING REMOTE SENSING
and features for the pre-symptomatic detection of changes in the physiological status of plants, developing, training, validating, and comparing predictive machine learning and deep learning models