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simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative
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questions and pertinent datasets, fitting and assessing models to the acquired data, performing simulations to test model assumptions, interpreting results, managing datasets and code workflows, communication
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within the LIENSs Laboratory a 1-year postdoc or a research engineer to improve the numerical modelling of nature-based solutions in coral reef and saltmarsh environments using the numerical modelling
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on developing a new multi-disorder prediction approach that integrates different sources of information. You work with analytical model development, extensive simulation studies and analysis of existing large
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for state-of-the-art techniques for Accelerated Nanomaterial Discovery, integrating synthesis, advanced characterization, physical modeling, and computer science to iteratively explore a wide range of
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methodologies, interfaces, and best practices to ensure model reusability and knowledge transfer across teams. Where to apply Website https://www.imec-int.com/en/work-at-imec/job-opportunities/systemctlm-modeling
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cover letter (maximum 1 page) Curriculum vitae (CV) Names and contact details of two references Copy of PhD degree certificate Union representatives: https://internt.slu.se/min-anstallning/facket
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) “Hope” probe. The position contributes to ongoing research, data analysis, simulation efforts, and dissemination of scientific results from Hope observations. The funding for this position is provided by
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. This includes conducting finite element modeling, ship resistance and stability assessments, as well as overseeing AM process and reporting. The role also involves designing and validating connectors
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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