523 algorithm-"Multiple"-"Prof"-"U.S" "NTNU Norwegian University of Science and Technology" Fellowship positions
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conditions. Our work combines traditional statistical methods with advanced artificial intelligence algorithms to identify patterns in disease. We also use qualitative methods to understand lived experiences
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Developing and integrating AI algorithms into the real development progress Preparing academic publications such as patent applications and research papers Contributing to quarterly and annual report writing
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and application of fast solvers for Maxwell’s equations and nonlinear inversion algorithms that we have already developed in a previous PhD project. In addition to electromagnetic geophysics
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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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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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electromagnetic data during drilling. This includes the further development and application of fast solvers for Maxwell’s equations and nonlinear inversion algorithms that we have already developed in a previous
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a Research Fellow to contribute to a project focused on algorithm design in Game Theory and Fair Division. Key Responsibilities: Formulate mathematical models for research problems in computational
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and RISC-V architectures. Investigate various aspects of system design, including: Topology exploration, routing algorithms, protocol and flow control design Chiplet oriented and interposer design
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architectures. Investigate various aspects of system design, including: Topology exploration, routing algorithms, protocol and flow control design Chiplet oriented and interposer design strategies, SiP and
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particular sequential, multiple assignment, and randomized trial design. You will have the opportunity to mentor the PhD students on the team. Experience in any of the following areas may be useful: SMART