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year project, funded by the DDLS program, we aim to develop AI-based tools in design of affinity ligands, such as the prediction of binding interactions between proteins. Data-driven life science (DDLS
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bioinformatic tools and focused on clear interpretation and communication of this data. This project is part of an exciting new Doctoral Training Programme in Microbial Genomics for Health Protection in
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in open fields, while also embracing data science, smart electronics/devices, computer programming as central to your work? Do you want to contribute to creating future-proof regenerative agricultural
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with a strong commitment to sustainability and cutting-edge research. You will be part of a team alongside expert supervisors: Professor Zecca, chair of healthcare technology, with 20+ years
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conferences and journals within machine learning and computer science. Your competencies M.sc in computer science, artificial intelligence, statistics, data science, or a closely related field. At least basic
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management, computer science, information management and beyond. Supervisors Primary supervisor: Professor Tom Jackson Secondary supervisor: Professor Ian Hodgkinson Entry requirements At least a 2:1 honours
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Foundation Data Science Collaborative Programme, “Synthetic health data: ethical development and deployment via deep learning approaches (SE3D),” which is a cross-disciplinary collaboration between Aalborg
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PhD Studentship: Experimental Investigation of Pharmaceutical Tablet Disintegration and Drug Release
. This project combines lab work, data analysis, and collaboration with experts in pharmaceutical science. Your findings will help develop better models to predict drug behaviour, ultimately leading to more
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of Technology offers strong support through a qualified supervisory team and a structured doctoral education program leading to a PhD in Geotechnical Engineering. Active participation in the international
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that you are particularly suitable for a PhD education. You must meet the requirements for admission to the faculty's Doctoral Programme Experience in analyses of imaging data such as structural MRI and