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stacking, both high performance and (very) high density can be achieved by this concept. The mission will be to explore, understand and develop the fundamental physics of device operation. This will require
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for: computers able to process information more like the brain, studying how to reproduce some of the properties of biological neurons and synapses using networks of molecules and nanostructures, and other
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of electrical engineering, Universite de Moncton (Moncton campus) Email: [mohamed.lamine.faycal.bellaredj@umoncton.ca] Context & Motivation: Because of their extreme computational needs where workloads demand
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research environment. Access to state-of-the-art equipment and facilities. Member of the integrated Research Training Group for dual mentoring, comprehensive doctoral training program, weekly seminars
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of neural hydrology, where hydrological models are directly learned from data via machine learning (e.g., LSTM neural networks, [1]). Initially, these models ignored all physical background knowledge and did
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should have or expect to achieve, at least a 2:1 (or equivalent) in any engineering degree programme, physics or mathematics. English language requirements: Applicants must meet the minimum
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fully funded PhD research studentship in Physics-Informed Machine Learning for Cardiovascular Medicine. This opportunity is open to UK (Home) candidates only. Project Overview Arrhythmias are disorders
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experimental molecular biology and cell biology is a merit, as is understanding of physics and structural biology, and experience in computational work including programming. You should be highly motivated and
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use of HPC for numerical computations Good background in Wave physics Good oral and written presentation skills in Norwegian/Scandinavian Personal characteristics To complete a doctoral degree (PhD), it
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/physics/biology) or engineering. The ideal candidate should have some understanding in the areas of Materials Science, Chemistry, Physics, Metallurgy, or Mechanical Engineering. The candidate should be self