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to correlate polymerisation kinetics, macromolecular architecture, morphological evolution and drug encapsulation mechanisms. Beyond experimental work, the project will integrate machine learning approaches
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; Proven competence on flow measurement techniques and PIV; Familiarity with optics, lasers, image processing and statistical data analysis Familiarity with flow modelling techniques (CFD) or machine
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; profile root and leaf microbiomes using amplicon sequencing; analyse integrated microbiome and phenotyping datasets; contribute to machine-learning models predicting pathogen invasion success and plant
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acquire in the course of the PhD skills in live microscopy, experimental neurobiology, genetics and behavioural work. The student will receive mentoring and have the chance to guide the research and
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modelling, machine learning, or microfluidics. They will also have excellent communication, organisational and problem-solving skills, and a strong interest in interdisciplinary quantitative biology
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and advanced machine learning. The project will integrate measurements from the SWOT satellite mission with Oxford's Global River Topology (GRIT) hydrography to develop verified, uncertainty-aware
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-learning models that connect powder characteristics and process parameters with the properties of the final components. These models will support faster feedstock qualification and enable predictive quality
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these processes requires AI and bioinformatics methods able to integrate heterogeneous, high-dimensional data. The ShadowEV-GBM project brings together spatial omics, metabolomics/lipidomics, molecular profiling
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initiates oscillations that regulate the cell cycle. How these two processes interact - and whether their interplay is precisely what allows the cell to coordinate repair with cell fate decisions - is
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characterization, quantitative image analysis, and assessment of treatment response. Within the research group, we value a positive work environment characterized by respect and consideration in our relationships