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establish a digital route to quantify the segregation behaviour of residual elements at austenite/austenite grain boundaries through atomic-scale simulations, using modern machine learning techniques and in
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sensors - if we can control and tune their properties. You will develop and use top-of-the-line machine learning models to predict the sensor response of these materials under realistic conditions
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microfluidic fabrication and experiments 3D printing machine learning. Demonstrated programming skills (Matlab, C++, or Python). Desired Demonstrated ability to work independently and to formulate and tackle
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Multi-omics data integration and workflow improvement Development and application of machine learning-based algorithms for the identification of antibiotics-associated proteins and antimicrobial
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qualified women. About the position The position contains both teaching duties and participation in research projects. The research project topics focus on improving object recognition through computer vision
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, machine learning, molecular dynamics, and fluid mechanics. We aim to understand how chemical structure of precursors and process conditions affect film quality, helping design better materials and
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and secondments. • Blended Learning Approach: Our training combines intensive in-person workshops at partner institutions with regular interactive online seminars, journal clubs, and research
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- Eng-PhD-05-25 Understanding Runoff Dynamics in Small Flash-flood-prone Catchments - Eng-PhD-06-25 Improved defect detection of Guided Wave Inspection Using Machine Learning-Driven Signal Processing
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. Current XAI methods are often generic and overlook industrial realities. This project will embed user-centric explanations directly into machine learning workflows using structured, ontology-driven
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/structural/mechanical engineering with experience and interest in structural dynamics, vibrational analysis, train-track-bridge interaction, signal processing, data science and machine learning. The successful