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opportunity to be equipped with applied research skill sets that are relevant to industry demands while working on research projects in SIT. The Research Fellow / Engineer (Building-Grid Interaction Design
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applications (https://dcm.univ-grenoble-alpes.fr/research/ingenierie-et-interactions-… ). The candidate will be based Grenoble, with secondments in other laboratories of the network. Grenoble is the largest city
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these innovations in ways that align with organizational goals and ethical standards. MINIMUM REQUIREMENTS Bachelor’s degree in design, Human-Computer Interaction, or related field. Minimum 3 years of professional UX
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 1 day ago
interactions. Topics include development of semantic web properties using contemporary programming techniques; standards-based design of responsive accessible systems; and production of rich media for online
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Master’s and/or PhD in Game Design, Interactive Media, or a related field with a track record of creative practice in the gaming industry. Past teaching experience at the Undergraduate Level is preferred
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qualification (usually PhD). Tasks: The successful candidate will contribute to one or more of the following focus areas: Human-Centered Design and Interaction: advance understanding of human sensory perception
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colleagues on multi‑omics data integration and analysis. You will also work with AI experts to help implement predictive models that improve guide design and functional genomics workflows. You will join an
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colleagues on multi‑omics data integration and analysis. You will also work with AI experts to help implement predictive models that improve guide design and functional genomics workflows. You will join an
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Job Description Field of research: Single particle cryo-electron microscopy and X-ray crystallization of viral glycoproteins, immunogen design, virology, biochemistry and biophysics. Purpose
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. In this project, we aim to develop digital tools combining density functional theory (DFT) and machine learning (ML) to accelerate the in-silico design of solid catalysts for the DA process. - Perform