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/develop predictive models that can inform future land management and conservation strategies. Responsibilities • Data mining: Compile, review and complete pollen data and age-depth models from existing
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infrastructure to support predictive analytics, recommendation, and dynamic pricing. Create pipelines and databases capable of aggregating and organizing information from multiple heterogeneous sources. O5
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discipline. Demonstrated expertise in clinical and biomedical NLP, including both predictive modeling and generative applications using foundation models Hands on experience building end to end ML systems
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highly motivated PhD student to develop advanced fracture models for predicting material degradation and failure in additively manufactured steel in nuclear reactor water environments. The project focuses
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The Doctoral Candidate will: Perform numerical modelling of the three NDE techniques to evaluate the influence of relevant material property gradients on each NDE observable generating a sizable synthetic
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machine learning algorithms for the prediction of manufacturing processes in composite materials. Development of user subroutines for finite element constitutive models Validation of model and numerical
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with a strong background in machine learning and LLMs, computer science, and modeling. The candidate will join the project “AI-driven predictive maintenance for buildings: Einar Mattsson (EM) - KTH
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, CRCF). It will develop AI tools to map and predict soil health across space and time, accelerate literature reviews, extract best management practices from long-term experiments, and design methods
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-package of the ChiExCo program, which aims to develop a reliable computational protocol to predict, for organic chromophores, both chirality quantifying factors (gabs and glum) resulting from excitonic
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accepted all year round Details Dynamic optimization is integral to many aspects of science and engineering, commonly found in trajectory optimization, optimal control (e.g. model predictive control, MPC