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Field
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one of the following areas is required: Numerical methods for large-scale, ill-posed nonlinear inverse problems Numerical optimization techniques Machine learning Strong programming skills in Matlab and
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/ML techniques for optimizing RFIC design and system performance would be an added advantage. Able to work independently and possesses strong research skills. Excellent verbal communication and
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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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, advanced optimization and control of semiconductor manufacturing processes and systems. Experience in working with advanced AI/ML software packages and super-computing cluster systems, including Hadoop
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biology is highly desirable. Practical experience in bioprocess engineering, including operation of bioreactors, optimization of microbial growth conditions, and management of continuous or batch processes
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Stimulated Raman Spectroscopy (PARS) method. Research tasks will include design, development and optimization of the full PARS system including a Raman cell pumping scheme, a photoacoustic cavity, and an
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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