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involved in related research projects. Collaborate with research partners and support laboratory activities. Job Requirements: PhD in Materials Science and Engineering, Metallurgy, Mechanical Engineering, or
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knowledge and proven capacity of data analytics and machine learning. *Excellent programming in Python, R, SQL. Have experience with tools such as Google analytics, AWS, Looker, Tableau, or similar
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machine learning (Dr Iñaki Esnaola, Electrical and Electronic Engineering) and advanced machining research (Dr Javier Dominguez-Caballero, AMRC Machining Group), with direct technical support from
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Graphics: https://www.jku.at/cg Your Qualifications: The successful candidate must hold a Diploma/Master’s degree in a corresponding discipline (computer science, mathematics, engineering or related) or have
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, test and measurement methodologies for electronic modules, system engineering, data pre-processing and database indexing/analytics for dashboarding/visualisation, embedding machine learning algorithms
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materials using statistical mechanics, molecular simulations, and machine learning. Expectations Candidates will be responsible for: Developing multi-scale modeling methods for polymeric materials, using
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or electrochemical system PhD in Chemistry/Materials Science/Physics Encourage initiating activities on MOF development, devising, and analytical process Experience in machine learning will be preferred Good oral and
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reproducible analysis workflows Familiarity with computational models of vision and machine learning methods (for example CNNs, deep generative models, encoding models) is preferred but not required Ability
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, engineers, PhD students, and postdoctoral fellows, at the interface between fundamental research, technological development, and experimental validation. Where to apply Website https://emploi.cnrs.fr/Offres
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Requirements Student enrolled in a PhD programme in Electrical and Computer Engineering Selection process Contest Evaluation Method(s) Curricular evaluation weighted to 50% on a scale of 20 points with a minimum