412 algorithm-development-"Multiple"-"Simons-Foundation"-"Prof"-"Embry-Riddle-Aeronautical-University" positions
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climate events in the Western US. CW3E is developing and implementing weather and climate observations, numerical models, machine learning algorithms, and decision support tools focused on atmospheric and
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that are relevant to industry demands while working on research projects in SIT. The primary responsibility of this role is to lead the research and development of a secure and privacy-preserving multimodal biometric
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other constituencies. Develop and maintain supporting documentation of measurement methods and algorithms, data sources and reporting processes. Conceptualize and develop cross-functional client/unit
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learning (ML) for high-fidelity data ‘stitching’. The integration of data from multiple analytical platforms is critical for advancing the understanding of complex biological and chemical systems. This work
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Benefits . Position Summary The position contributes to the conceptualization, design, development and delivery of biomedical informatics research projects, addressing all areas of clinical and translational
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developing an AI machine learning algorithm to analyze how experimental parameters ( 3D printing and electrospinning) affect the design. Work with PhD students in data collection (physical and mechanical
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, research website maintenance, clinical coordination in experiments, group meeting minutes organization Aiding in developing an AI machine learning algorithm to analyze how experimental parameters ( 3D
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(PNT) and geolocation systems definition, design and development. The PNT and geolocation domain covers the end-to-end (E2E) engineering of PNT and geolocation systems at system, space, ground and user
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will apply state-of-the-art machine learning algorithms and custom disease-relevant genomic datasets (e.g., coronary artery single-nucleus chromatin accessibility and RNA sequencing) to develop targeted
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Bioinformatician at the intermediate or senior level. The Breuss laboratory is focused on employing and developing statistical machine learning, bioinformatic algorithms, and related approaches for the detection