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Field
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research Managing and analyzing data, implementing machine learning algorithms on data Conducting literature reviews Preparing presentations, manuscripts, and grant submissions Assisting with research
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structure-preserving methods for mathematical models of physical systems, including topics such as geometric numerical integration, finite-volume and finite-element methods, variational formulations
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well as part of an ENCODE functional characterization center. The lab is especially interested in non-coding cis-regulatory elements (CREs) and the variation within them, using high-throughput experimental
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elements of the job description in your supporting statement. Following the closing date of the vacancy, you will be notified on the outcome of your application in due course. Our Offer We have a wide range
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
of samples for whole-genome sequencing; and microbial bioinformatic analysis of sequencing data, including genome assembly and annotation, detection of resistance genes and mobile genetic elements, and
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materials, and surface analysis methods. Key Duties & Responsibilities 1) Metal nitride synthesis – 25% 2) Reaction product characterization- 25% 3) Set up, fix, and operate equipment- 20% 4) Writing journal
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. Research Component: Conduct research on modelling and analysis of electric power grid Study impacts of high-fidelity grid-forming inverter models and high-voltage direct current (HVCD) models on power system
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analytical and numerical models of nonlinear vibration systems. The successful candidate will investigate stochastic resonance and noise-assisted dynamics, perform finite element modeling of coupled mechanical
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computing and artificial intelligence. Areas of interest include, but are not limited to, the following: Quantum Machine Learning and AI: Develop novel quantum algorithms and computational frameworks
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systems. • Develop and implement predictive models for packaging performance using machine learning approaches and physics-based simulations. • Investigate logistics optimization strategies for packaging