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to the development and use of new in vitro model systems for neurodegenerative diseases and brain cancer. The group is led by Henrik Ahlenius and includes 3 postdocs, an associate researcher, and a number of master’s
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principled new models and methods, for modern machine learning problems. Machine learning recently has been largely advanced by differential equation-based frameworks, such as generative diffusion models
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, and genome‑modifying technologies. The research group works on several projects related to the development and use of new in vitro model systems for neurodegenerative diseases and brain cancer
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model for large scale assessments and projections of the land-water carbon cycle to variation in climate conditions. The detailed direction of the PhD studies will be discussed and decided jointly with
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involved in several of these cutting-edge research projects and in the societal transformation that they entail. We offer a broad range of courses and study programmes to match the skills in demand. We hope
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utilizing Python and web-stack technologies (such as JavaScript), to translate theoretical models into functional, testable software in close collaboration with our core research team. Beyond software
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and CH4) from headwaters, and use of machine learning and process-based model for large scale assessments and projections of the land-water carbon cycle to variation in climate conditions. The detailed
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solutions, followed by parametric optimization and modelling, along with characterization studies. According to the Higher Education Ordinance, a person appointed to a doctoral studentship should primarily
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transformations. The project investigates a hybrid approach that combines deep learning with grammatical inference to develop models that are interpretable, efficient, and mathematically verifiable while leveraging
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from vascular lesions and blood, combined with genetic, clinical/epidemiological and imaging parameters from patients. We also perform in depth functional studies in animal and cell culture models