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collaboration with industry partners. This work will apply optimal control theory, including machine-learning algorithms and Bayesian estimation, to coherent control of nitrogen-vacancy centers in diamond
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repertoires within the thymic niche. Identify molecular and signaling targets to guide the engineering of functional thymic environments and optimize T-cell output from pluripotent stem cell-derived progenitors
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the thymic niche. Identify molecular and signaling targets to guide the engineering of functional thymic environments and optimize T-cell output from pluripotent stem cell-derived progenitors. Automate
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many-body physics, spin dynamics, quantum simulation, quantum optimal control, quantum optics, atomic physics. Required Skills: PhD in a field of research related to experimental quantum physics Broad
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. The successful applicant will train directly under the supervision of Dr. David Granville, PhD, and will have an opportunity to develop their leadership skills in the laboratory by participating in overseeing
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biology for a two-year term with possibility of extension. The successful applicant will train directly under the supervision of Dr. David Granville, PhD, and will have an opportunity to develop
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Citizenship Canadian Permanent Resident International Degree Level Doctoral The Google PhD Fellowship Program was created to recognize outstanding PhD graduate students doing exceptional work in computer
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, modelling missing data for IPDMA, estimating sample parameters from incompletely reported data, and using optimal test assembly methods to create short-form measure; and (3) meta-research on bias from data
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at the BC-CfE. The PKMS laboratory is led by Dr. Anil Maharaj and focuses on optimizing the safe and effective use of drugs in underserved populations through modelling and simulation. The BC-CfE is a world
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of Pharmaceutical Sciences and the Laboratory team at the BC-CfE. The PKMS laboratory is led by Dr. Anil Maharaj and focuses on optimizing the safe and effective use of drugs in underserved populations through