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modeling, secure data exchange, and reconfigurable production architectures. The research is carried out in collaboration with leading industrial and academic partners within large-scale Horizon Europe
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on two main lines of research. The first concerns the modeling of general dark matter–electron interactions in detector materials. This will be achieved by combining methods from particle and solid state
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to the development of methodologies for modelling, predicting, and validating dynamic interactions through numerical simulations and field measurements. This project is funded by The Swedish Transport Administration
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focus is the interplay of these factors with mitochondrial translation systems and respiratory chain complex assembly. We use the yeast Saccharomyces cerevisiae as our primary research model. In
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finite element modeling as the foundation of a comprehensive design framework that integrates simulation, experimentation, and machine learning. Fall-related injuries are a leading cause of morbidity and
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of quantum-chemical simulations are strongly desired. The candidates experienced in software for photophysical simulations (MOMAP, FCclasses, or custom handmade codes) are prioritized. In order to communicate
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with industry, academia, authorities, and insurance companies. Main responsibilities Computational Modeling and Simulation Develop and validate finite element models using LS-DYNA, OpenRadioss
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patterns of genomic sequences, with applications ranging from biogeographical mapping to paleogenetic reconstructions. The candidate will work jointly with Dr. Eran Elhaik to design machine-learning models
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sequences, with applications ranging from biogeographical mapping to paleogenetic reconstructions. The candidate will work jointly with Dr. Eran Elhaik to design machine-learning models that unlock
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. Eran Elhaik to design machine-learning models that unlock the potential of genomics for forensic investigations and historical reconstructions. Work duties We aim to develop machine learning methods