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Nature Careers | Government of Canada Ottawa and Gatineau offices, Ontario | Canada | about 1 month ago
Intelligent Cell Simulation System that uses artificial intelligence (AI) to model how living cells behave, respond to therapy, and interact within their tissue environments Non-Linear Wavefront Sensing AI
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circuit topologies for high-efficiency DC-DC conversion. Calculate resonant tank parameters to achieve ZVS/ZCS and optimize switching performance. Model and simulate resonant circuits under varying load and
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the computational simulations; 3) Implementing and analyzing in the computational model the known effects of magnetism on tumor biology, evaluating its influence on cell behavior and tumor progression; 4) Using
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the mechanistic modelling of pharmaceutical tablet disintegration. This UKRI/EPSRC-funded project offers a unique opportunity to join a dynamic research team collaborating with a consortium of academic and industry
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conducting experiments in both simulation and real-world robotic systems. Job Requirements: Preferably PhD degree in Computer Engineering, Computer Science, Electronics Engineering or equivalent. Independent
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design: difference-in-differences and fixed effects models, instrumental variables, treatment selection, power analysis, analysis of attrition and non-compliance, analysis of heterogenous effects and
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experience in presenting scientific work. Preference for candidates with experience in: -Complex networks and systems modelling and simulation. -Discrete dynamical systems (cellular automata, agent-based, etc
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approached by tools in both computational chemistry and applied mathematics. Specifically, the project involves the development of mathematical modeling strategies at both numerical and analytical level to
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. Under this job title, you will develop a CFD modelling platform to evaluate the combustion, flammable and toxic gases emission and dispersion characteristics resulted from battery fires/thermal runaway in
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The Atmospheric Chemistry Research Group (ACRG) and School of Engineering Mathematics at the University of Bristol have developed GATES, a graph neural network (GNN) machine learning model that can