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noise, allowing only specific algorithms with relatively shallow quantum circuits to be executed. In the NISQ era, hybrid algorithms run partially on quantum computers and partially on classical computers
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settings. Develop and test algorithms for object detection, tracking, and classification using LiDAR sensors. Help guide and mentor graduate students and other junior team members working on the project
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university/spinout environment. This is a unique opportunity to work at the forefront of applied research and innovation, helping translate novel control algorithms and hardware prototypes into real-world
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. Responsibilities* Design, implement, and evaluate LiDAR-based experiments in lab and real-world settings. Develop and test algorithms for object detection, tracking, and classification using LiDAR sensors. Help
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decision making, while you will be capable to apply machine learning and computational algorithms of social choice. This post is associated with following projects: Embedding EDI in the Distribution
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state-of-the-art facilities to work on the following: Developing advanced path planning, search, and exploration algorithms for multi-UAVs systems in unknown and complex 3D environments. Designing
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This research project aims to establish the theoretical and algorithmic foundations of quantum adversarial machine learning, an emerging field at the intersection of quantum computing and machine learning. It
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an opportunity for a Postdoctoral Fellow. You will contribute to UNSW’s research efforts in developing machine learning algorithm for photovoltaic applications and utilising them for the investigation
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 23 days ago
Python or R is preferred. Ability to analyze multi-omic data, including microbiome data, would be an advantage. Ability to build algorithms and data pipelines would be ideal. Create a Job Match for Similar
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dataset from various internal and external resources. ● Understand and apply best-in-class algorithms to address biological and clinical questions. ● Collaborate effectively within an interdisciplinary team