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SPECIFICS Postdoctoral Researcher in Defect Modeling The Pennsylvania State University Department of Nuclear Engineering The Computational Nuclear Materials Group (https://sites.psu.edu/cnmg/ ) at Penn State
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) prediction models to ensure the safety, efficiency, and longevity of lithium iron phosphate (LFP) batteries. Key Responsibilities: Develop and implement machine learning algorithms for SOC and SOH estimation
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around the identification of small molecules using mass spectrometry data, and the use of language models to predict the existence of undiscovered small molecules that are likely to be observed by mass
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include the development of integrated modelling approaches to better predict the effects of habitat modifications. This should provide significant benefits by allowing assessment of development and
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In Vitro Predictive Models to Explore Tendinopathy”. The project is funded by the Medical Research Council (MRC) and part of the organ-chip research work underway within the Centre for Predictive in
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assessment models and evaluate the nutritional quality of foods. Build risk-benefit assessment models to quantify and predict the health impacts of new food products. A central case study will explore
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candidate will work on cutting-edge research integrating genome-scale language models (GenSLMs) with deep mutational scanning data, and experimental virology to predict viral evolution and identify emerging
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results and conduct analytical modelling and numerical simulations (e.g., finite element modelling) to support experimental findings and predict performance. Independently implement and execute the research
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 8 days ago
funds (HORIZON EUROPE Workplan: The researcher will work on gravitational waves and the physics of black holes, including searches for physics beyond general relativity and the standard model, as
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genomic data, the lack of advanced analytical frameworks has hindered forensic efforts. This project aims to develop and apply AI-based methods to predict the origin and dispersal patterns of genomic