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. This includes exploring the use of digital twins for bioreactors and deploying AI driven predictive models to improve optimisation, consistency and overall yield. The main focus for this role is to work with the
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project involves interdisciplinary research at the interface of computer science and mathematics, with a focus on bivariate molecular machine learning for modeling molecular interactions and properties
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mechanisms of adaptive and acquired drug resistance, exploring network-level control and feedback in cell signaling systems, identifying novel drug targets and therapeutic strategies, and developing predictive
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processes, targeting annual savings of £280,000. Responsibilities include creating and refining models to predict particle behaviour, calibrating them to 95% accuracy, and establishing sensor systems for real
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 6 hours ago
profiling (BGC-Argo) floats (https://www.nature.com/articles/s41586-021-03805-8). The NASA Ocean Biogeochemical Model (NOBM) has recently been coupled to the Subseasonal to Seasonal Prediction Version 3 (S2S
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of pharmaceutical formulation and manufacturing processes. The role The post holder will develop and implement mechanistic models to analyse and predict the behaviour of pharmaceutical processes. Your work will
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, version control) and numerical workflows. Experience programming for data analysis and model workflows (e.g., Python, MATLAB; FORTRAN/C familiarity for model configuration). Demonstrated verbal and written
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of £280,000. Responsibilities include creating and refining models to predict particle behaviour, calibrating them to 95% accuracy, and establishing sensor systems for real-time data acquisition. You will
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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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into the wave interaction and propagation processes. Modelling the propagation characteristics of optical communication systems with a focus on optical atmospheric turbulence and statistical prediction models