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),. Familiarity with data analytics, machine learning, digital twin knowledge, or Python programming language. Knowledge of additive manufacturing, process physics, thermodynamics, and/or metallurgy to interpret
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learning, or optimization Strong programming skills in Python and experience with scientific computing and machine-learning libraries Ability to work across experimental, robotic, and computational systems
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for a short-term 2.5-month postdoctoral position (full-time) focused on developing and validating machine learning models that predict soil health and crop performance. The position will exploit datasets
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enable students to earn associate or bachelor’s degrees through a combination of in-person, online or blended learning. All of our system institutions place strong emphasis on service — helping to build
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: This project, which is part of the EPSRC progarmme grant on Quantum advnatage in Quantiative Quantum Simulation (QQQS), will involve adapting and using our existing ultracold erbium and potassium machine
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) calculations, microkinetic modeling, and machine learning methods, and in developing machine learning interatomic potentials (MLIPs). Minimum Qualifications: Ph. D. in Physics, Materials Science, Chemistry
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mathematical foundations in Data Science/Machine Learning. Job Related Minimum Required Education and Experience Requires a Doctoral (Ph.D.) degree in area of research specialty by the start date of employment
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mathematical foundations in Data Science/Machine Learning. Job Related Minimum Required Education and Experience Requires a Doctoral (Ph.D.) degree in area of research specialty by the start date of employment
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Finance at EPFL seeks postdocs to reinforce our research in quantitative finance with a focus on applied machine learning. Main duties and responsibilities Working and collaborating on research projects
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Czech Technical University in Prague, Faculty of Nuclear Sciences and Physical Engineering | Czech | 3 months ago
, design of functional polymer-based and composite coatings, and the effects of sterilisation and storage, machine-learning and AI models for alloy design and property prediction, with experimental