28 modelling-and-simulation-postdoc PhD positions at Chalmers University of Technology
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from automated vehicles (AVs), they must be both safe and appreciated by drivers. This project uses modeling (e.g., AI/machine learning) and human behavior data to predict perceived safety and quantify
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importance, for triggering shallow landslides in sensitive clays. The focus will be on developing computational models that will quantify the mechanisms, precursors and the time to failure. This will be
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interfaces. About us The Condensed Matter and Materials Theory division models and studies diverse materials and condensed matter systems, often inspiring new materials, technologies, or structured media with
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The aim is to unravel the anthropogenic and natural processes, and their relative importance, for triggering shallow landslides in sensitive clays. The focus will be on developing computational models
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models. Experience in analyzing CFD data and interpreting simulation results. Excellent command of written and spoken English. Experience writing scientific reports and presenting research findings
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evaluation frameworks and/or the development of energy system optimization models. The research is applied and closely linked to industrial interests and needs. About the research Our research aims to provide
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PhD Position in Theoretical Machine Learning – Understanding Transformers through Information Theory
Transformers are central to many of today’s most successful AI models, from language understanding to computer vision. Yet, their success remains largely empirical, with limited theoretical understanding
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researchers, postdocs, and PhD students working toward a sustainable energy future through comprehensive systems modeling and scenario evaluation. Main responsibilities Conduct high-quality research within
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Technology Laboratory (QTL) division of the Microtechnology and Nanoscience (MC2) department, working in a large team of PhDs, postdocs and researchers. About the research We are seeking PhD students to work
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on the hypothesis that the future of building design lies at the intersection of physically sound building simulation models and machine learning (ML) techniques. Key considerations include effectively integrating ML