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to the SLAC scientific mission. Panofsky Fellows push the frontiers of existing research programs at SLAC and develop their research group and program to expand into new directions building on this foundation
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applications in diverse disciplines. The Fellows program aims to identify and train intellectual leaders to shape the field of physical artificial intelligence and its applications. The program provides
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scientific supervision: The work plan will be developed in the ISLab laboratory (Synthetic Intelligence Laboratory), Department of Informatics, Algoritmi Centre, School of Engineering of the University
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United Kingdom Application Deadline 30 Nov 2025 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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to create tools to validate and evaluate the modules developed, as well as to integrate them into the existing web application for testing, editing, and validating the conversation system. This allows
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 3 months ago
to appoint a Postdoctoral Fellow/Research Fellow in User Experience Design (0.5 FTE) and/or Web Development (0.5 FTE). The position can be offered as one full-time role or two part-time appointments for a
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United Kingdom Application Deadline 30 Oct 2025 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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pilot context. The work plan consists of the following activities: 1) Analysis and specification • Study of the legal domain supported by AI4JURIS and the technologies in use. • Development of priority
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. Working plan: Objectives and Work Plan: Production of animated educational videos in microbiology Development of laboratory tutorial videos in microbiology/microbiomes Recording, editing, and post
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plan: The work consists of developing models for the prediction of biological control agents (BCAs), using different approaches: Machine Learning (random forests, support vector machines, lasso), Deep