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learning analyses, but also with other types of analysis. The work involves supporting Swedish researchers under a user fee-based support model. The projects will differ in complexity and length and will
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are suitable. The aims of this project are to Review operating characteristics proposed for rare disease trials Develop novel Bayesian operating characteristics for different types of rare disease trials Apply
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other empirical damage and vulnerability data. Couple the ABM with the Regional Flood Model (RFM) to describe temporal developments of flood risk considering adaptation decisions. Different adaptation
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problems in health data science. Air pollution is composed of several different environmental pollutants, for example particulate matter (PM10 and PM2.5), ozone (O3), nitrogen dioxide (NO2) and sulphur
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projects, including: The post-holder will run numerical models that simulate the dispersion of greenhouse gases through the atmosphere. These models will be used, in Bayesian inference frameworks
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an inclusive working environment. We believe this can be achieved through attracting, developing, and retaining a diverse range of staff from many different backgrounds who have the ambition to create a
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with statistical modeling (ideally Bayesian statistics) • Proficiency in Fortran, R, Python, Matlab, or ideally other common languages (e.g., C/C++) Strong computational skills Strong oral and written
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-traditional, e.g., event data) and network structures (for sensor networks). In this project, we will investigate Bayesian deep learning approaches to training models under uncertainty for several sensing
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plants they visit and pollinate. Bayesian networks (BNs), and other probabilistic graphical models, can provide a visual representation of the underlying structure of a complex system by representing
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ML work, and allows attacking new applications which make a difference, for instance in scientific research. Different team members have different expertise, and by working together are able to address