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: questionnaires on healthcare and patient outcomes, qualitative data on patient experiences, improved diagnostics using innovative laboratory methods, development of pathophysiology-based treatment plans
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and Saeys teams. In this research project you will develop and apply algorithms to link clinical phenotypes of metastasis to molecular phenotypes in mouse models. It is known that metastases exhibit
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behaviour through these models using uncertainty quantification/machine-learning (UQ/ML) algorithms To optimise the manufacturing process with the help of the simulation tool To support in the development and
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Carl von Ossietzky Universität Oldenburg | Oldenburg Oldenburg, Niedersachsen | Germany | 21 days ago
conservation related consequences of animal navigation, and (4) links biological and technical systems through models, algorithms, and devices. The acquired knowledge can help to solve major societal questions
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using innovative laboratory methods, development of pathophysiology-based treatment plans, identification of biomarkers and haemostatic modifiers with multi-omics approaches, an AI-driven bleeding
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coalitions for delivering reliable, low-carbon energy services. Collaborating closely with UK Power Networks, SSE Energy Solutions, and the University of East London, you will develop robust economic Model
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data Design algorithms for correlating low-level events into process-level attack models Contribute to joint framework development with TU/e on continual learning Collaborate with industry partners
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participate in the training and dissemination events of the MSCA Doctoral Network ANT . You will develop signal processing algorithms and protocols for AirComp, and implement and validate them in simulation
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will focus on developing a bioinspired, AI-driven BCI system that enables intuitive and adaptive control of a wearable wrist-hand exoskeleton for stroke rehabilitation. The system will integrate
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to develop intelligent systems that are both data-efficient and physically consistent. The successful candidate will contribute to advancing novel methodologies that integrate domain knowledge with learning