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in computer vision, machine learning, and related fields, to further visualization and interpretation of molecular images. Our research environment focuses on methodological development in cryo
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methodologies in brain diseases. The candidate will work on developing advanced new algorithms, testing and validation, and applications in these data modalities. The candidate will have the opportunity to work
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scientists, biomedical informaticians, clinicians, and public health researchers to develop deployable, trustworthy methods that improve patient outcomes and health system operations. Key responsibilities
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transmission of information and energy, systems theory, and computational hardware and software. ECE students are encouraged to develop synergies with disciplines outside of engineering. The candidate should
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associate will participate in the design and implementation of the reference data model to ensure simulation system interoperability. Additionally, they will develop AI algorithms and multi-criteria
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of this PhD project is to develop machine learning algorithms that perform efficiently and coherently across both classical and quantum computing platforms. The PhD project falls under the collaboration between
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Doctoral Candidates (DC1 and DC2) to carry out research in neuromorphic photonic-electronic integrated circuits for brain-inspired information processing and sensing (DC1) and in the development of efficient
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Welding in Vacuum We are developing a laboratory robotic simulator for testing stud welding in high vacuum (≥ 10⁻³ Pa) for space applications, with future utilization directly in orbit. The system includes
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turbine blades. Successful re-development for end-of-life composites could enable reuse in other structural applications. This PhD will investigate the development of hierarchical Bayesian algorithms