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and assessing weld quality of mechanical parts in real-time by developing machine learning models that use sensor data and other tasks that are assigned to you. Core Responsibilities: Understanding
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. Demonstrated expertise in using machine learning and optimization frameworks in conjunction with FE simulations to assist with component and/or process design is preferred. Excellent written and oral
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Post-Doctoral Associate in the Division of Engineering (Mechanical Engineering) - Dr. Mohammed Daqaq
and experimental, in the broad field of nonlinear mechanics. Preference will be given to applicants with expertise in machine learning, wave propagation, metamaterials, and/or fluid–structure
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analytics, including correlation analysis and machine learning techniques. Preferred Qualifications: Experience with microstructure characterization techniques (SEM, EBSD, TEM, XRD). Experience in mechanical
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simulation, machine learning, battery manufacturing, electrochemical systems, materials characterization, or computational mechanics A strong publication record, preferably including at least three first
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Postdoc (f/m/d) Mechanical Characterization and Materials Assessment for Nuclear Applications / P...
data analysis and statistical methods # Interest in or initial experience with data-driven methods or machine learning is an advantage # Strong communication skills and a collaborative approach to
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | 2 months ago
methods or machine learning is an advantage # Strong communication skills and a collaborative approach to working with internal and external partners # Independent, structured and solution-oriented way
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required to meet the eligibility criteria. Of secondary importance are: Experience with Python and relevant libraries for machine learning, optimization and simulation. Documented expertise in simulation
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Post-Doctoral Associate in the Division of Engineering (Mechanical Engineering) - Dr. Mohammed Daqaq
to applicants with expertise in machine learning, wave propagation, metamaterials, and/or fluid–structure interactions. Applicants must hold a Ph.D. in Mechanical Engineering or a closely related discipline, with
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. Demonstrated expertise in using machine learning and optimization frameworks in conjunction with FE simulations to assist with component and/or process design is preferred. Excellent written and oral