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Field
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algorithms, environmental time series and anomaly detection. SALARY: Up to $6,250.00 Monthly ($75,000.00 Annual, Approximately) NOTE This is a grant funded position with continued employment contingent upon
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aims to optimize the operations (serving) of AI by developing algorithms that manage compute, network, and storage resources in a carbon-efficient way while supporting long-term benefits
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using jets and heavy-flavor probes. The candidate will also participate in algorithm and software development for the LHCb trigger system as part of the Real Time Analysis Project. A Ph.D. in experimental
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. Minoru Otani). The post holder will undertake the following tasks: Contribution to the design, implementation, testing and demonstration of new methodologies and algorithms for the study of electrochemical
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learning algorithms into professional software with an intuitive user interface, incorporating feedback from CHWs through iterative design and evaluation cycles. The selected candidate will be part of a
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project. The project goal is to develop bacterial sensors with the ability to communicate with novel sentinel plants as a readout for changes in environmental conditions or changes in the rhizosphere
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NSF funded projects, advancing the knowledge about distributed systems, developing novel algorithms for distributed resource and workload management, simulating and emulating systems, as
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academic and professional qualifications Proven research experience in the field of modelling and analysis of biological networks Solid foundation in mathematics and algorithmic design Strong programming
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design, development, and validation of material, control systems, and algorithms for next-generation soft haptic actuators and experiences. Note that the research involves significant interactions with
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cloud computing tools and big data workflows A track record of completed research projects Well-written, peer-reviewed papers are expected Interest in using EHR metadata, wearable sensors, and process