62 phd-mathematical-modelling-population-modelling Postdoctoral positions in United Arab Emirates
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research areas represented at NYUAD. The successful applicant will also receive a mobility credit to participate in conferences. Applicants must have a PhD in Mathematics, with a strong background in one
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models to analyze and mitigate fine particulate matter (PM2.5) exposure from various infrastructure systems (e.g., transportation networks, manufacturing systems, and truck routing). Assessing
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have a PhD in Civil Engineering, Engineering Mechanics, or Mechanical Engineering. Applicants are expected to demonstrate research experience in the fields of structural modeling and machine-learning
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New York University in Abu Dhabi, Mathematics/Science Position ID: NYUAbuDhabi -POSTDOCTORALFELLOW [#26802] Position Title: Position Type: Postdoctoral Position Location: Abu Dhabi, Abu Dhabi
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modeling and characterization for communication and sensing in emerging spectrum for 6G and beyond, with a focus on the FR3, THz and optical frequency bands. This research will be conducted under the joint
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. We are seeking a Postdoctoral Researcher to join the team and make significant contributions to the field. The researcher is expected to have (i) strong machine learning skills to improve model
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, beamforming techniques for FR3 and THz bands, reconfigurable intelligent surfaces (RIS), and integrated sensing and communications (ISAC). The research methodology will involve the development of mathematical
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Responsibilities The PDA will conduct research to design and develop optical wireless communication systems. This involves the development of mathematical models for signal transmission/reception, derivation
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are particularly interested in candidates with a strong mathematical background and expertise in one or more of the following areas: High-dimensional probability and concentration/functional inequalities Markov
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systems (ITS). In particular, the successful candidate will conduct cutting-edge research in: Developing physics-informed neural networks (PINNs) for complex dynamical systems modeling and observer design