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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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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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to scale up and demonstrate sustainable processes for industrial (bio)manufacturing of pharmaceuticals by integrating environmentally friendly technologies and processes. However, given the complexity
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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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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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. A non-deterministic AI machine learning model for the identical task would not offer this demonstrability or, critically, the repeatability of classical algorithm-based systems. Furthermore, there is
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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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introduce new challenges in design and control. Their development is still largely bio-inspired and trial-and-error based. Integrating flight and actuation control in SAMs is particularly complex. DOMINANTS
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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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: Investigating how functional motifs are encoded in HS chains and how they influence their biological activity. Using gastruloids as a model system with which to study GAG structure/function relationships