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Field
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qualifications As our new colleague in our research team your job will be to develop novel computational frameworks for machine learning. In particular, you will push the boundaries of Scalability, drawing upon
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for the efficient formation of high-value compounds. Advanced NMR methods and computational data analysis will be compounded to devise novel reactions towards pharmaceutical precursors, polymer building blocks and
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PhD candidate in Design and synthesis of pyrophosphate mimetics to study enzymes involved in natural
Universities). Leiden University offers an attractive benefits package with additional holiday (8%) and end-of-year bonuses (8.3 %), training and career development. Our individual choices model gives you some
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nanoparticles and reactions at the atomic-level by combining path-breaking advances in electron microscopy, microfabricated nanoreactors, nanoparticle synthesis and computational modelling. The radical new
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to mechanical forces. We work with leading international groups on modeling and also conduct simulations at DTU. Our overarching goal is to understand and predict the mechanical behavior of metals during plastic
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application for our PhD Stipend. At the Faculty of Engineering and Science, Department of Chemistry and Bioscience, a Ph.D. stipend is available within the general study program. The Ph.D. stipend is open for
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for downstream tasks. In this project, you will develop novel unsupervised machine learning methods to analyse cardiovascular images, primarily focusing on MRI. In your research you will train models to learn a
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begore the deadline. The start date is 1st October 2025. This studentship is related to a multi-institutional EPSRC Programme Grant "AMFaces: Advanced Additive Manufacturing of User-Focused Facial
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experimentation and finite-element modelling. Research themes would be flexible including green steel formability under the EPSRC ADAP‑EAF programme for automotive and packaging applications; or micromechanical
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models