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models for multiple chronic diseases in real-world data and cohort studies. To successfully work in this position, experience of data-driven analytical approaches, machine learning and advanced
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dysfunction. We combine genetic, molecular, and physiological approaches to identify and dissect the pathways that mediate these effects. The successful candidate will conduct original research using the model
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regenerative capacity of liver tissue. Your work will involve the use of research animals to investigate cell-cell interactions during regenerative processes and to developed new in vitro organoid models. You
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and collaborative research within one of the project’s research themes. Develop, apply, and document quantitative models and analyses of electricity markets, energy systems, or flexible demand
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of Arctic Phytoplankton, within the Research Council of Finland-funded project on "Climate-driven Dispersal, risk or potential for Arctic diversity". The position is full-time, and the preferred starting date
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Caro's group, whose core expertise is the development of machine-learning-infused atomistic modeling techniques and their application to important problems in chemistry, physics and materials science
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research. Its focus areas are technology, health, and society. The university regards the competence of staff as its most important strategic asset and supports its development. For further information
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interests, the work may include: Developing new models and methods for noise-driven wireless communication. Designing and evaluating low-power and low-complexity signaling schemes for future IoT and 6G
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computational approaches to uncover mechanisms underlying complex diseases and to develop predictive, clinically actionable models. The successful candidate will work in a highly collaborative environment with
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Toxicology (MAT) research group, a newly established group led by Dr Alexandra Schaffert. MAT develops next-generation, animal testing-free approaches to chemical safety, combining molecular biology