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Field
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independently and collaboratively Experience with deep learning frameworks, such as Tensorflow or Pytorch is advantageous Effective communication skills and an interest in contributing to a highly international
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In the appointment of CGPS Governance Intern, your general duties and responsibilities will be: Assisting with the development of the College’s annual governance calendar, including coordinating governance committee activities, scheduling meetings and preparing agendas. Maintaining accurate...
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Your Job: At the Institute for Advanced Simulation – Data Analytics and Machine Learning (IAS-8) we are looking for a PhD student in machine learning to work within a project linked to the
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descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange isotherm parameters directly from molecular properties. These predictions will be integrated
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Posting Title Graduate PhD Student (Year-Round) Machine Learning Applications for Cyber-Physical Power System Operations Intern . Location CO - Golden . Position Type Intern (Fixed Term) . Hours Per
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5 Feb 2026 Job Information Organisation/Company Chiba University Research Field Astronomy Chemistry Mathematics Physics Geosciences Researcher Profile Recognised Researcher (R2) Application Deadline 20 Feb 2026 - 23:59 (UTC) Country Japan Type of Contract Permanent Job Status Not...
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experience in machine learning and parallel computing Good organisational skills and ability to work both independently and collaboratively Experience with deep learning frameworks, such as Tensorflow
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27 Feb 2026 Job Information Organisation/Company Osaka University Department Graduate School of Information Science and Technology Research Field Engineering Information science Technology Computer science All Other Researcher Profile Recognised Researcher (R2) Application Deadline 10 Mar 2026 -...
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machine learning (ML) along with data from previously solved problem instances to solve new, yet similar, instances more efficiently than with general purpose algorithms such as Netwon`s method. In
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heavily relies on empirical determination of key model parameters. By combining protein structure descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange