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background in CMOS/VLSI design, computer architectures (preferred RISC-V architecture), and deep learning principles. Experience with industry-standard EDA tools such as Cadence suite: Genus, Virtuoso, Spectre
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) or Machine Learning models. These tools will be integrated with physics-based models of environmental loading (waves and wind) to enhance the accuracy and robustness of the assessment. All components assembled
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(e.g., Kalman Filter) or Machine Learning models. These tools will be integrated with physics-based models of environmental loading (waves and wind) to enhance the accuracy and robustness
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used will be Density Functional Theory, statistics, machine-learning and dynamics. Collaboration with members of other research groups at UCPH and abroad is required. Who are we looking for? We
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Applicants should hold a relevant MSc degree in electronics, electrical engineering, computer engineering, or related fields. Required Qualification: Solid background in digital CMOS design and deep learning
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computationally efficient numerical structural models. To support the condition (state) assessment, the project will also explore the use of advanced estimators (e.g., Kalman Filter) or Machine Learning models
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electrode-structure to encounter optimum electrolyte as well as electrical flow. Prototyping of the identified structures via stereolithographic, 3D printing and textile techniques like tufting, machine-based
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Policy Implications and Recommendations Case Studies of Successful Innovation Funding Methods The project will employ a combination of methods, including machine learning (ML) and generative AI (GenAI
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doctoral candidate who meets the following requirements: A background and strong interest in aluminum alloys, fatigue analysis, and numerical modelling is preferred. Experience with computer aided design and
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needed to achieve these temperatures, the control cables connecting to the qubits are often more than 2 meters long. At the same time, a quantum computer powerful enough to solve problems beyond the reach