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Field
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This self-funded PhD research project aims to advance the emerging research topics on physics-informed machine learning techniques with the targeted application on predictive maintenance (PdM
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exciting opportunities for machine learning to address outstanding biological questions. The PhD student to be recruited will be working on the development of machine learning methods for single-cell data
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bioinformatics and modeling of the complex microbial communities that shape our world. We are seeking a highly motivated PhD student to develop scalable probabilistic machine learning models and algorithms
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. Please submit these documents as a single pdf. Please include “PhD Application (Interpretable Machine Learning)” followed by your name in the subject line. The application CV should, at minimum, include
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. This project will rely on recent advances in neural networks to develop machine learning potentials (MLPs) for MD simulations of realistic nanomaterial/coolant-liquids and use these to gain fundamental insights
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PhD Position in Theoretical Machine Learning – Understanding Transformers through Information Theory
Join us for a fully funded PhD position in theoretical machine learning to uncover how and why transformers work. Explore their inner mechanisms using information theory. As part of this project
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Description At the Leibniz Institute of Plant Biochemistry in the Department of Bioorganic Chemistry a position is available for a PhD in Machine Learning for Enzyme Design (m/f/d) (Salary group E13
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on the broad topic of Machine Learning and Psychophysiological Deception Detection. The studentship is part sponsored by GCHQ and funded for up to 3.5 years with fees and a stipend at the standard UKRI rate
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often fail to preserve the fidelity of combined datasets, leading to loss of crucial information. This proposal aligns directly with the CAMS Data Analytics Theme and the Grand Challenge of using machine
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of this WASP-financed project is machine learning, in particular dealing with generative models and instabilities associated with cycles of retraining on mixtures of human and machine-generated data