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Field
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, epidemiologists, clinicians and lab researchers, with expertise in the field of prediction modeling, longitudinal data analysis, statistics, data science, machine learning, AI, organoid models and cystic fibrosis
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that thrive with imperfect data, creating adaptive models that can quickly learn from new machines with minimal training data, and integrating these predictions with optimization algorithms to make cost
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monitoring will also be integrated into this PhD. Thermal prediction models are currently implemented on a Field Programmable Gate Array (FPGA), while the thermal PI controller (which will be further developed
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for donor kidneys. Central to this is the use of machine learning to evaluate the predictive value of biomarkers from various sources: donor-related data, perfusion fluid, and kidney biopsies. Kidney biopsies
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organization. We invite applications from motivated candidates with passion and a strong interest in conducting research and innovation activities on crop modeling, remote sensing, and computer vision to predict
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expertise in the field of prediction modeling, longitudinal data analysis, statistics, data science, machine learning, AI, organoid models and cystic fibrosis. The supervisory team will consist of dr. Maarten
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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
-like AI models which can predict DNA structures Performing experiments for validation Participation in conferences in Germany and abroad (incl. presenting your research results) Preparing scientific
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mandatory core focus, the PhD position allows room for the individual research interests of the applicant to shape specific aspects—whether in modeling strategy, applied machine learning methods
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that are transforming many sectors today through language models, recommendation systems and advanced technologies. However, modern machine learning models, such as neural networks and ensemble models, remain largely
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patient samples. The Sheffield arm of the project will develop statistical and machine learning models to identify and validate predictive biomarkers of resistance evolution in Pseudomonas aeruginosa lung