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. Research in the lab is highly multidisciplinary and quantitative, requiring development and use of cutting edge computational modeling and statistical analyses (including machine learning and artificial
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for part-time employment. Starting date: 14.01.2026 Job description:PhD position on physics-based machine learning modeling for materials and process design Reference code: 2026/WD 1 Commencement date
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frameworks. Familiarity with machine learning or AI methods applied to imaging. Research Data Management and documentation practices Statistical Analysis & Experimental Design Technical Communication (reports
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and in vivo models. The prospective candidate will have opportunities to work within a highly interactive, multidisciplinary team. Preferences will be given to candidates who Have some relevant research
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expertise in sensor design, experimental testing, modelling, machine learning, and industrial collaboration, positioning you to contribute directly to next-generation inspection technologies for aerospace
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Professor / Associate Professor / Assistant Professor / Research Assistant in Quantitative Marketing
inference, or machine learning techniques; and (ii) are open-minded and committed to teaching excellence at both undergraduate and graduate levels. Applicants for the Research Assistant Professor position
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Simulation – Data Analytics and Machine Learning (IAS-8) at Forschungszentrum Jülich, which is dedicated to pushing the boundaries of data science theory and application. Our research spans from use-inspired
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datasets with machine learning methods, and software development are beneficial Good organisational skills and ability to work systematically, independently and collaboratively Effective communication skills
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staffing models. Skills / Knowledge / Abilities Basic computer knowledge, MS Windows, Word, Outlook, and clinical applications. Supervisor experience is preferred but not required. Does this position have
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graduate students. The Teaching Professor will also teach within the Cognitive Science Department, contributing to their existing curriculum in machine learning, data science, and computational modeling