58 algorithm-development "https:" "Simons Foundation" research jobs at McGill University
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contributing to the design and development of advanced models, algorithms, and AI‑driven systems for renewable energy microgrids and data centers. The Research Assistant will work closely with laboratory
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Switzerland. Develop a prototype hardware implementation of different signal processing algorithms on FPGA, optimized to significantly improve the resolution of real-time energy measurements made by the ATLAS
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. In addition, we will explore the possibility of using LIDAR sensing to strengthen the semantic SLAM pipeline. The developed algorithms will be integrated with the perception pipeline developed by our
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-language scientific and technical programming, including Python, C++, Java, Embedded C, LaTeX, and Swift, for developing algorithms, simulations, and research workflows. Skilled in research methodology
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registry), previously developed algorithms (e.g., converting all opioids dispensed to morphine milligram equivalents), and epidemiological and econometric methods (e.g., interrupted time series analysis
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candidate will be principally responsible for the design, development and quality control of the data analysis in consultation with lab members and under the supervision of the principal investigator. Data
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ABIF can be found at: http://www.mcgill.ca/abif. Primary Responsibilities 1. Technical Development & Innovation Lead and document quality control (QC) workflows for ABIF imaging systems, including
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at professional conferences, and career development. Qualifications: BSc or MSc degree (Psychology, Neuroscience, Computer Sciences, Medical Physics, Biomedical Engineering, or related field) Other Qualifying
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metastatic progression. The Garner lab studies how changes in the bone marrow during myeloid cell development influence their function and contribute to disease progression. Our goal is to identify new
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evidence from diverse sources, analyze international survey data, and contribute to the development of a conceptual framework that captures the multi-level and intersectional nature of stigma. Findings from