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Your job Are you looking for a PhD position where you develop state-of-the-art machine learning methods for the life sciences (geometric deep learning, transformer-based approaches, ...) with a
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a small team of software developers working on AI prototypes and infrastructure. There is no teaching load in this position. Would you like to learn more about what it’s like to pursue a PhD at
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the power of data and machine learning! Job Description We are seeking a highly motivated PhD candidate to join our research team focused on Collaborative Metadata Management for Large Data Repositories
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by drift-diffusion will be treated at the atomistic scale and vice-versa. A deep understanding of device physics, numerical modelling, and computer programming are, therefore, required. The PhD student
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-based machine learning prediction model; Processing imaging data (18F-FDG PET/CT and DW-MRI / DCE-MRI scans); Validating the accuracy and reliability of the prediction model; Optimizing and validating
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neuroimaging (fMRI, MEG, iEEG) and behavioral data. This PhD position is part of the Natural Auditory Scenes in Humans and Machines (NASCE) project, funded by the ERC Synergy Grant, and hosted at Maastricht
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programming applications (e.g., experimental design, machine learning for science). It will do so by bringing together a diverse team of PhD candidates with a primary focus in three different areas: programming
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(crowding, concentration of nutrients, size of liposome, etc.) with gene expression levels across a synthetic genome. To achieve this aim, you will use VASA-seq and Ribo-Seq to generate large data sets
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differentiable programming applications (e.g., experimental design, machine learning for science). It will do so by bringing together a diverse team of PhD candidates with a primary focus in three different areas
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differentiable programming applications (e.g., experimental design, machine learning for science). It will do so by bringing together a diverse team of PhD candidates with a primary focus in three different areas