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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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optimization frameworks that adopt an interdisciplinary approach, integrating concepts from operations research, transport modeling, welfare economics, transport justice and machine learning. You will be based
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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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), aiming to bridge neuroscience and electronics. The project integrates expertise from circuit design, machine learning, and neurotechnology to deliver innovative solutions for applications such as brain
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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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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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for intelligent brain-computer interfaces? We are offering a PhD position in analog/mixed-signal CMOS circuit design for EEG and wearable sensor interfaces, as part of a pioneering project focused on assistive