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signaling models”. The scholarship is full-time for 2 years, with access starting in May 2026 or by agreement. The research will be carried out in the laboratory of Cemal Erdem at the Department of Medical
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imaging, computer vision, and predictive modelling. The postdoc will further develop an existing rumen‑fill scoring algorithm into a functional prototype and pilot the technology for longitudinal monitoring
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management. Data from case studies (inspections, monitoring, and experimental tests) are used for model updating, calibration of safety formats, and prediction of future performance and remaining service life
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and predictive confidence, including sensitivity and identifiability analyses Compare grey-box models against purely mechanistic and purely data-driven approaches Optimize model performance
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roughness, AM roughness is characterized by randomness, porosity, and powder adhesion, producing flow behaviors that existing correlations and turbulence models fail to predict. Understanding and modeling
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flow behaviors that existing correlations and turbulence models fail to predict. Understanding and modeling these effects is crucial for industrial applications such as gas-turbine internal cooling
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strategies. The research group focuses on exploration of tumor immune microenvironments through spatial omics and imaging, development of computational models for prediction of molecular and clinical features
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). The project focuses on developing computational models for cancer risk assessment, integrating multiple types of data and risk factors. The main objective is to design and apply machine learning and deep
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University Work duties: This postdoctoral position is part of the AFLOW consortium supported by the Swedish Energy Agency and focuses on materials modelling of chemical stability in aqueous organic redox flow
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) and Lundberg’s Lab at the School of Chemistry, Biotechnology and Health (CBH). The project focuses on developing computational models for cancer risk assessment, integrating multiple types of data and