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for this position, the following is required: PhD in a relevant field such as computer science, quantum physics, electronic engineering, data science, AI, machine learning, Earth system science, climate etc. with a
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to apply Website https://www.academictransfer.com/en/jobs/354994/postdoc-neuro-inclusive-partici… Requirements Specific Requirements You have a PhD in Industrial Design, Human–Computer Interaction, Design
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partners to reduce CO2 emissions in steel production using machine learning. You can find more information here . You will work on a theoretical and an applied project on data-enhanced physical reduced order
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above. Education in related fields such as data science, AI, computer science, machine learning, space engineering, Earth system science, climate, etc. would be an asset. Additional requirements In
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) for engineering systems and structures, as well as expertise in machine learning, stochastic modeling, and Bayesian statistics. Programming Skills: Proficiency in programming languages such as Python, C, or R
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degree in engineering, mathematics, or a related field, with a strong background in prognostics and health management (PHM) for engineering systems and structures, as well as expertise in machine learning
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strategies (e.g. predictive or machine learning approaches) to improve performance and reduce costs. Collaborating with industrial partners on design optimization, life-cycle analysis, and business case
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Village to calibrate and validate models. Investigating control strategies (e.g. predictive or machine learning approaches) to improve performance and reduce costs. Collaborating with industrial partners
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light/heating modules, and selection and sorting routines. Guided by machine learning, we will perform directed evolution experiments where we optimize the synthetic genome that encodes for a biological
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applications* in close collaboration with other discipline experts (software, microelectronics and applications engineers). * except for RF payloads. ** including artificial intelligence and machine learning