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integrating them with microfluidics as a standalone device. This is a great opportunity to learn new skills, contribute to assay development, and intellectually contribute to projects within a collaborative
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techniques, or tight-binding approaches. Proficiency with major simulation packages such as ASE, Quantum ESPRESSO, VASP, CP2K, or LAMMPS, and their Python interfaces. Working knowledge of machine learning
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-binding approaches. Proficiency with major simulation packages such as ASE, Quantum ESPRESSO, VASP, CP2K, or LAMMPS, and their Python interfaces. Working knowledge of machine learning techniques in
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proceedings as defined in the project scope and agreements. Scientific activities include: 1) Develop Tidal Resource Models Design and implement statistical and machine learning tidal models to predict energy
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environment. In this role, you will lead the computational strand of the project, applying molecular simulations, data analysis, and machine learning to uncover how molecular structure, charge, and surface
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tools, collaboration with project stakeholders, and engagement with the consortium and Defence and Security stakeholders. Technical Requirements: Strong coding skills with background in machine learning
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from Deepfakes Project. We are looking for a software/machine learning engineer (or similar) to work in an interdisciplinary team reporting to Dr Sophie Nightingale (Principal Investigator). The Project
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knowledge within the realm of political science. They should be proficient in conducting quantitative analyses. Experience with large language models, machine learning, and/or programming in R or equivalent
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closely related field (e.g. Chemistry, Physics, Biophysics, Biochemistry, Molecular biology, etc) Computer Skills Academic and research software relevant to job duties (Linux operating system, Gaussian/ORCA
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members. Learn more at https://hr.duke.edu/benefits/ Duke is an Equal Opportunity Employer committed to providing employment opportunity without regard to an individual's age, color, disability, gender