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Field
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candidates whose work lies at the intersection of statistics, machine learning, data analytics and modern AI algorithms. This includes, in particular, statistics for high-dimensional and complex data
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on developing innovative algorithms and models to address complex problems in diverse fields such as robotics, healthcare, and finance. The department offers a range of undergraduate and graduate programs
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building sensors, analyzing data, and writing data summaries. The position is 35 hours per week for a three week duration (7/7 – 7/25). Duke University is an Affirmative Action/Equal Opportunity Employer
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collection methods for external data (e.g. web scraping) to collect data for use in modelling and analysis. Develop analytic plans, engineer supporting algorithms, write new functions or applications in
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data retrieval, interface with multiplexers and sensors. Demonstrated proficiency with eddy covariance instrumentation, and ecophysiological field instrumentation. In particular, candidate has worked
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Status: Nonexempt Work Schedule: Flexible Summary The intern will receive training on machine learning algorithms, implementing them, and fitting them on the datasets in our cluster computers. Overall
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educational resources and practices that support replication. Desirable: F1 Experience in analog circuit design, e.g., RF circuits, power electronics, sensor interfaces etc. F2 Software skills including one
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and foster economic opportunity in Arizona and across the globe. This role supports irrigation research through field and laboratory work, primarily focused on soil and plant sampling, sensor
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the University’s STEM annex in Markeaton Street, where staff offices and lab facilities are co-located. Key investments in the last few years include dedicated intelligent instrumentation and smart sensor
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algorithms for very high speed coherent passive optical networks (VHSP) Implementation of DSP algorithms in MATLAB/C++, that can be used for ASIC development Development of our DSP software tools Research work