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analytical models Physics-informed machine learning for deformation modeling and prediction Integration of perception, planning, and control for robust real-time robotic performance Requirements Ph.D. in
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personal career support equal opportunities For more information see the LEAD AI webpage or send an email to leadai@uib.no Qualifications and personal qualities: Applicants must hold a PhD degree according
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of the evaluations and investigations, and iii) assisting with the oversight of graduate and undergraduate assistants working on these projects. Qualifications Basic qualifications Must have a PhD in thermal science
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qualifications Must have a PhD in thermal science and engineering, or related field, and research experience in phase-change heat transfer. Strong written and verbal communication skills are required. Preferred
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diverse data sets, applying advanced analytics, and leveraging ML/AI techniques to detect, quantify, and forecast global risks affecting sourcing strategies. It will also include assessing AI-driven demand
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Required Qualifications: PhD in Neuroscience, Bioengineering, Electric Engineering, Computer Science, Physics, or a related field Strong quantitative and analytical skills Experience in either experimental
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communication and scientific writing skills. • Ability to manage multiple data sources and integrate them into complex analytical frameworks. • Autonomy in planning and executing research tasks Benefits 31.000
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17 Sep 2025 Job Information Organisation/Company ERATOSTHENES CENTRE OF EXCELLENCE Department Big Earth Data Analytics Research Field Computer science Engineering » Computer engineering Computer
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functional theory (DFT) or tight-binding modeling of materials, as demonstrated in application materials. NOTE: To be appointed at the Postdoctoral Researcher title, it is necessary to have the PhD in hand
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Essential Requirements: - PhD Degree in a relevant scientific field (e.g. computer science, data science, mathematics, engineering, or related); - Strong understanding of generative models (e.g., VAEs, GANs