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defor-mation monitoring from geospatial time series data, complex topographic and landscape dynamics, and the de-velopment of data-driven methods to study natural hazards, climate-related changes, and
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Start Date: Between 1 August 2026 and 1 July 2027 Introduction: This PhD is aligned with an exciting new multi-centre research programme on parallel mesh generation for advancing cutting-edge high
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fully funded PhD position within the LowDataML doctoral network, focusing on developing innovative machine-learning approaches for drug discovery under low-data conditions. LowDataML aims to bridge
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expand current technology to include automated live analysis, integrating machine learning algorithms capable of interpreting the complex behavioural patterns of mussels in response to environmental stress
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focuses on developing cutting-edge statistical/machine learning methods for fitting complex, multi-institutional network models to partially observed hospital infection data. This research will directly
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play a leading role in the analysis of large and complex genetic and electronic health records datasets with a range of Statistical and Machine Learning approaches, whilst leading a broad range of
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: Developing physics-informed neural networks (PINNs) for complex dynamical systems modeling and observer design Creating and validating digital twin architectures that incorporate physical laws and constraints
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City University of New York, Advanced Science Research Center Position ID: CUNY-ASRC-PROF [#31365] Position Title: Position Type: Open Rank Position Location: New York, New York 10031
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and innovations that complements and strengthens existing research on this topic within the NCT WERA and the One NCT. Particular, the professor will streamline the workflows for complex early clinical
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be working primarily with scientific machine learning methods, including symbolic regression and neural networks. You will apply the algorithms to the discovery of new models in different fields