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Field
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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optimizing fabrication and testing processes for new devices. Qualifications PhD degree in Electrical Engineering, Materials Science, Mechanical Engineering, Biomedical Engineering, or a related field with
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Python, TensorFlow, PyTorch, or similar frameworks. MS or higher degree (PhD preferred) degree in Computational Neuroscience, Computer Science, Bioinformatics, or a related field with demonstrated ability
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, develop, and optimize new methods and techniques to address critical project or functional area needs. Participants will improve existing or develop new laboratory methods and processes, read and adapt
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. Ensure optimal progress of experiments in the laboratory by conducting, supervising, and evaluating laboratory procedures and outcomes. Revise and analyse targets to obtain high-quality and reproducible
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or supervision of labs and tutorials. Job Requirements: MSc/MEng (Research Associate) or PhD (Research Fellow) in Electrical and Electronic Engineering or other related fields. Expertise in RFIC design, including
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, develop, and optimize new methods and techniques to address critical project or functional area needs. Participants will improve existing or develop new laboratory methods and processes, read and adapt