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Field
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, computational modelling, and data-driven alloy design to: Understand the mechanisms of local austenite-to-ferrite transformation in low-alloy steels; Develop frameworks to predict and control
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in providing data-driven insights, they often fail to use visualization as a powerful means of interpretation and communication. Most process mining tools provide static and linear representations
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Biomechanical Engineering, biomedical engineering, technical medicine, or a related field. Strong interest in musculoskeletal modelling, human experimentation, and medical imaging. Solid programming skills (e.g
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models such as Random Forest and Neural Networks to help understand and predict pairwise interactions between pollinators and plant species. - Software Engineering: integrate models into a standalone
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Materials science and technology are our passion. With our cutting-edge research, Empa's around 1,100 employees make essential contributions to the well-being of society for a future worth living
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-on monitoring with cutting edge data-driven and physical based models, including the deployment of machine learning algorithms. The project aims to have a tangible impact on the way urban waters are monitored
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facilities and competences covering the whole chain of manufacturing, characterization, mechanical testing and modelling of fibre composites. Responsibilities and qualifications Do you have a strong research
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Harnessing Nonlinear Dynamics: From Data-Driven Discovery to Engineering Job description Nonlinear dynamics lies at the centre of many mechanical systems, from large-scale structures to nanoscale
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will develop generally applicable machine learning models and methods driven by the data-rich experiments from our collaborators. The real-world use-case is to learn the rules that govern the dynamics
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: Department of Chemical & Biological Engineering, Faculty of Engineering Main Supervisor: Professor Sankar Bhattacharya Employment Type: Full-time Duration: 3.5-year fixed-term appointment Remuneration: $36,000