146 electrical-machine-design-"https:" "https:" "https:" "https:" Fellowship positions
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, navigation, and behavioural state machine of the embodied AI agent within the XR scene. Implementing features for environment manipulation (allowing users to re-design spaces via agent assistance) and
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qualification, EQF Level 7) in Computer Engineering, Electrical and Computer Engineering, or a closely related scientific field is required. The candidate must demonstrate, through their curriculum vitae and
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: Professional Officers Employment Type: Fixed Term Job Purpose The Research Fellow / Research Engineer (with focus on MedTech / HealthTech Engineering) will support applied research and innovation in community
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/Anders Lien 9th September 2026 Languages English English English PhD Research Fellow in Machine Learning for Cognitive Neuroscience Apply for this job See advertisement About the position Position as PhD
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APPLICATION INSTRUCTIONS: CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please login to Workday to complete the internal application process . Please do not apply
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the following conditions: Scientific Area: Computer Science. Admission Requirements: Academic qualification: Master's degree in Computer Engineering, Computer Science, Electrical and Computer Engineering, or
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://www.di.ubi.pt ), under the following conditions: Research Field: Machine Learning/Pattern Recognition Objectives: Unsupervised Incremental Representation Learning for Ground-Based Drones in Dynamic Industrial
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Current University of Arkansas System employees, including student employees and graduate assistants, need to log in to Workday via MyApps.Microsoft.com , then access Find Jobs from the Workday
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; Internet-of-things; relevant laboratory equipment. Required Qualifications REQUIRED QUALIFICATIONS: 1. Ph.D. in computer engineering, electrical engineering, computer science, or closely related areas from
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees