72 distributed-algorithm-"Meta"-"Meta"-"Meta" positions at University of Toronto in Canada
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oversees revenue sharing distributions. As Senior Financial and Operations Analyst, you will support a broad complement of financial management activities, including financial modeling, analysis, and
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, and applying judgement in decisions on distribution, consulting as appropriate. The incumbent provides a broad range of support to the Office Manager in administering the operation of the Office of
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, screening and reporting on items, assigning priority, handling routine matters falling within established policies/practices, and applying judgement in decisions on distribution, consulting as appropriate
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of our student residences. Responsibilities include greeting residents/guests and visitors, answering questions, transferring phone calls, distributing mail/parcels, monitoring building access, issuing
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tenant letters; the collection and recording of rental payments, residence database maintenance; handling work orders; key control; mail distribution; answering routine residence queries and ordering
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missing data Gathering information for test/examination packages for invigilators Tracking and monitoring the distribution and return of examinations Resolving issues within the scope of the role and
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promising candidates with computational tools and machine learning algorithms, and elucidating structure-property relationships of emerging molecules, polymers, solid-state materials, formulations, etc. Tasks
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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
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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
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 2 months ago
statistics, contingency tables, normal probability distribution, and sampling distributions. The second half of the course introduces inferential statistical methods. These topics include significance test for