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required. Candidates should be comfortable developing and teaching the core MADS courses offered by the Computer Science Department (CSC 501: Algorithms and Data Models; CSC 502: Systems for Massive Datasets
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of a clinical sequencing platform. Primary Responsibilities: Use and develop analysis pipelines using in-house and third party tools or algorithms, and incorporate them into specialized analysis
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Government of Canada | Government of Canada Ottawa and Gatineau offices, Ontario | Canada | about 1 month ago
and proceedings, presentations at top conferences, and widely recognized contributions to software projects. • Designing, implementing and testing new algorithms or applications in one or more
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(e.g., SolidWorks, AutoCAD) and printer operation. Proficiency in coding languages such as MATLAB, Python, or C/C++, with the ability to develop custom scripts and algorithms for data analysis and
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experience of database optimization and best practices. Strong understanding of data structures and algorithms such as Arrays, Linked Lists, Stacks, Queues, Searching and Sorting algorithms. Demonstrated
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of technology infrastructure for data access requests and the documentation, storage, and re-use of algorithms and existing data; the development of data analytics and data collection and analysis infrastructure
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prototypes. Signal Acquisition & Processing Develop acquisition pipelines to capture vascular motion during CPR. Implement signal‑processing algorithms to extract markers of blood flow. Model Development
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, Tableau, AWS QuickSight, IBM, Google, etc Technical Expertise in three or more of the following competencies AI & Machine Learning: Proficiency in ML algorithms, deep learning architectures, NLP, computer
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findings regularly. Prototype algorithms, validate outputs, and document methods clearly Collaborate asynchronously with an international team, present findings regularly Experience with remote sensing
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characterize large quantities of candidate molecules, calibrating theoretical models with experimental data, predicting promising candidates with computational tools and machine learning algorithms, and