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of the immune system in retinal degenerative disease. We utilize induced pluripotent stem cells, human donor tissue and mouse models of retinal degeneration to study the role of monocyte derived macrophages in
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and accuracy, ultimately saving lives. This collaborative PhD project aims to develop and evaluate advanced deep learning models for speech and audio analysis to predict Category 1 emergencies
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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interdisciplinary research team. We study tumor evolution and immune microenvironment adaptation by combining functional genomics, experimental model systems, patient samples, and computational biology (Brägelmann et
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advanced operational strategies, such as model-predictive control, tailored to dynamic prosumer energy demand. Foster collaboration: Work closely with industrial and research partners, including CENAERO
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, engineers and PhD candidates. The PhD candidate is expected to develop an advanced engineering noise prediction model for efficient computation of sound propagation in a range-dependent atmosphere where
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heavier than their fossil fuel powered counterparts. A framework that can accurately model complex dynamics and generate projections for future scenarios is essential for understanding the impact of changes
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computational modelling to be used to design and re-engineer flower architecture. The RA's main focus will be on computational modelling of gene regulatory networks for predicting the mechanisms leading
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) offer new avenues to tackle this problem. AI models have demonstrated strong potential in clinically relevant insights from electrical signals such as ECGs, and from cardiac imaging modalities including
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The Network Analysis and Modelling uses machine learning to investigate how genetic variation shapes gene regulation, protein function, and, ultimately, observable plant traits. We are seeking a