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at the RMIT Melbourne CBD campus About the Role We are seeking a Postdoctoral Research Fellow to join RMIT’s Materials Modelling and Simulation group to apply classical Molecular Dynamics and Machine Learning
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related to battery materials, correlated electron calculations, including via DFT+U, supercells, dynamical mean field theory or experience in defect and/or alloy calculations, machine learning, and other
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/or machine learning methods, and an interest in applying these tools to urban and housing policy questions. The Fellow should demonstrate potential for producing high-quality research and a strong
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cardiovascular care using advanced machine learning techniques, including deep learning. Informal enquiries may be directed to Dr. Dimitrios Doudesis, Principal Investigator (Dimitrios.Doudesis@ed.ac.uk
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writing/presentation Job Requirements PhD degree in an engineering field related to this project Experience in dynamic modeling, machine learning and optimization & controls Having basic knowledge in carbon
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Engineering, etc.), expertise in cutting-edge AI and machine learning is essential; while structure prediction or materials chemistry experience would be advantageous, it is not a pre-requisite for the role
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annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based approaches, machine learning) where
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Jan. 2026, based in the University of Birmingham UK. This position will use further develop the novel AI/machine-learning (ML) approach in Chen et al. (2022 & 2024, Nature Geoscience ) and apply
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, and may utilise iterative algorithms, machine learning and high-performance computing. Through the Monash Centre for Electron Microscopy, opportunities exist to acquire large experimental datasets using
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 6 hours ago
.) Departmental Preferred Experience, Skills, Training/Education: Ph.D. in design-related discipline, specializing in deep learning and biometrics. A strong background with evidence of experimental and AI/machine