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real-time, and will be tested and demonstrated on a state-of-the-art HiL rig and an autonomous test vehicle. The post is focused on the development of automotive-grade algorithms and estimators that will
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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of diagnostic laboratory genetics. While the emphasis of the program is on the diagnosis of heritable genetic disorders, trainees will be exposed to various somatic applications as well. The fellowship experience
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 1 day ago
: PD&PEWER Level A&B Postdoctoral - Research Fellow _ CSS.pdf This position will provide opportunities to: Conduct cutting edge research in machine learning, AI and algorithms, such as but not limited
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the emphasis of the program is on the diagnosis of heritable genetic disorders, trainees will be exposed to various somatic applications as well. The fellowship experience will cover a wide range of molecular
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interaction and analytics using AI. Key Responsibilities: Development of artificial intelligence (AI) technologies to perform human-robot interaction and analytics System integration of the developed algorithms
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research Facility located at Warwick QLD. Key responsibilities will include: Research: Establish a research program, collaborate on research projects, seek and manage research funding, publish in reputable
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | 44 minutes ago
Position Information General Information Position Number POST40 Working Title Postdoctoral Fellow - Computer Science Division Academic Affairs Department Coll of Computing&Informatics (Col) Work
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within Algorithms and Extremal Combinatorics At the Department of Informatics at the University of Bergen, there is a vacancy for a postdoctoral research fellow position within Algorithms and Extremal
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(terrestrial and NTN). The goal of this research is to design and develop algorithms and techniques that adapt to the environment, minimizing signaling overhead associated with channel estimation and enhancing