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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 2 months ago
. Duties/Responsibilities Analysis (50%) Develop machine learning algorithms to analyze ground magnetic field perturbations Analyze the results using machine learning interpretability techniques
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provide support to the PI (Asst. Prof. Kean J. HSU) and other project team members on overall research administration for the projects, including coordination of research staff and trainees based on a lab
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engine. Algorithm Development: Contribute to designing, prototyping, and testing algorithmic models for personalized content recommendations. System Evaluation: Support the evaluation of the recommendation
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learning algorithm to analyze how experimental parameters ( 3D printing and electrospinning) affect the design. At least 3 semesters of FIU work experience or related experience outside the University with
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candidate may be employed at Grade E or Grade F depending on qualifications. The project is led by Prof. David Crundall. You will work with a small team to help create and validate our training course, and
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through the integration of high-performance materials, robotics, automation, computation, and process optimization. Affordable mass customization of building designs that are produced with advanced
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The Saarbrücken Graduate School of Computer Science provides an optimal environment for pursuing doctoral studies in computer science at an internationally competitive level. As a student, you
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2025 HDR Scholarship - Intelligent Sensing & Perception Laboratory Postgraduate Research Scholarship
Laboratory is offering a unique opportunity for a motivated student to pursue an industry embedded PhD in target tracking and sensor fusion. The project will involve an intensive research programme supported
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recognized research program with interdisciplinary connections across the CRI network at UT and ORNL. Contribute social science expertise to collaborative studies on the transition to circular bioeconomy
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nationally and internationally recognized research program with interdisciplinary connections across the CRI network at UT and ORNL. Contribute building and construction expertise to collaborative studies