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Field
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seeking a doctoral researcher possessing a curious and self-driven mindset to develop and apply new simulation protocols to push towards realistic and dynamic modeling of AS-ALD processes. Recently, we have
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, we create knowledge, innovations, and solutions that help move the world forward. Faculty Mechanical Engineering From chip to ship. From machine to human being. From idea to solution. Driven by a deep
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engineering, physics, materials science or a related area. Experience with modeling, experiments and design. Team players with good communication skills. Creative, independent and self-motivated. with
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biology or a quantitative discipline, such as physics, engineering or applied mathematics, with expertise in programming and an interest in developing AI-driven computational tools for biological data
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German Cancer Research Center in the Helmholtz Association (DKFZ) | Heidelberg, Baden W rttemberg | Germany | 3 months ago
, genome-wide data integration, statistical modeling, and hypothesis-driven experimental design, preparing them for leadership roles at the interface of molecular biology and data science. Your Tasks Project
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Fully Funded PhD Studentship (Home Fees) Project Title: Developing Fair and Robust Cancer Risk Prediction Models Using Health Data Supervisor: Professor Angela Wood (Professor of Biostatistics and
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1 Aug 2026 Job Information Organisation/Company Wageningen University & Research Research Field Engineering » Electrical engineering Researcher Profile First Stage Researcher (R1) Application
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, or expect to achieve: A Bachelors degree in Chemical Engineering, Mechanical Engineering, or a closely related discipline with an award of First Class or 2.1 Experience in, or willingness to learn modelling
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Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. | Dortmund, Nordrhein Westfalen | Germany | 3 months ago
Developing computational framework for 3D shape modeling towards biological insights Developing AI-based representation learning framework for data-driven cell modeling Report findings and methods in
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-driven model selection, and deep learning for data analysis and feature extraction from characterisation data. Surrogate modelling will be employed to reduce computational costs, and AI-based uncertainty