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molecular biology, genome editing, and imaging techniques to study cardiovascular phenotypes. -Collaborate closely with the group leader and other team members, contributing to project planning, data
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methods such as large-scale sequencing Multiplex IHC and image analysis Data analysis and programming in R Independent interpretation of results and acquisition of new knowledge and methods Active
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(mathematics, statistics, AI&ML, systems engineering). Supervise and support PhD students and postdocs involved in joint projects. Help establish new collaborations with industry and public-sector stakeholders
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. Alexandre Graell i Amat), and Recorded Future (Dr. Johan Östman). Our team currently consists of 5 PhD students and 2 postdocs. Research visits to Chalmers and Recorded Future will be organized throughout
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, spectroscopic signatures, microstructural images, processing conditions, and macroscale performance will be used for the optimization of materials. The candidate will collaborate extensively with in
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or plant phenotyping, or image analysis. Experience with isotope tracing, physiological measurements, or nutrient analysis. Skills in statistical modelling (e.g. R), multivariate analysis, or trait-based
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algorithms for Bayesian machine learning with applications in e.g., medical image analysis. The doctoral student position is offered within the machine learning project “The Challenges for Machine Learning in
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imaging, mathematical modelling, and functional genomics, receiving experimentally testable predictions generated by state-of-the-art predictive models. These predictions will be rigorously validated using
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. They have led to a plethora of important downstream applications, such as image and material generation, scientific computing, and Bayesian inverse problems. At the core of these models are differential
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terrestrial laser scanning data (ALS, TLS), satellite and aerial images. Collecting field data as well as participating in burning experiments may also be part of the work. The research team includes expertise