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Field
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experimentation via laboratory automation and III) AI navigation of the complex landscape described. This powerful combination not only supports the creation of AMP based nanomedicines, but also addresses a central
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/Qualifications - Automation or applied mathematics background, with a strong interest in physical models and numerical method - Analysis of partial differential equations, variational approach, Bayesian estimation
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the risk of missed defects. Using the power of Artificial Intelligence (AI), this research aims to: - Automate defect detection in complex 3D structural data - Enhance diagnostic accuracy and processing
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. The role of the young researcher will be to implement an automated identification tray for a black box EMC model, with the doctoral student implementing the model validation simulations in a system
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About the ProjectProject details: Next-generation networks are rapidly outscaling the capabilities of traditional management paradigms. While early AI/ML models offered a degree of automation, they
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PhD student (m/f/d) in the field of chemistry, chemical engineering, materials science or comparable
engineering. We are looking for talented people to join us. Your responsibilities include: Setting up automated, laboratory scale reaction processes for synthesizing and optimizing nanoparticles and
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Subject area: Drug Discovery, Sustainability, Laboratory Automation, Microfluidics, Machine Learning Overview: This highly interdisciplinary 4-year funded PhD studentship will contribute to cutting
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of the superconducting re-buncher for the third cryogenic module of HELIAC. Another important task will be the design and implementation of an automated test stand for superconducting cavities of HELIAC, which
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harness advanced techniques such as machine learning, optimization algorithms, and sensitivity analysis to automate and enhance the mode selection process. The result will be a scalable methodology that
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on the development of AI-supported and human-in-the-loop methods for automating and optimizing software instrumentation processes. Drawing on a strong background in software engineering, data analytics, and machine