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About the Role We are seeking an enthusiastic and motivated postdoctoral researcher to apply advanced data analytics and machine learning techniques to real-world clinical data in the field of viral
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with these collaborators. The research will involve both analytical work and numerical computations. The balance between analytical and numerical type work is flexible and can depend on the preferences
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application of analytical pipelines to data generated by the WaSPP network. What you would be doing This is a three-year post that would suit a biologist or mathematician (or similar) with research experience
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scholars in Law and two PhD students (one in Law and one in Computer Science/Data Analytics), as well as with international, European and national stakeholders involved in the CURE project. The post-holder
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existing machine learning methods, as well as building robust, well-documented, and reproducible analytics pipelines for long-term use by the wider team. You will carry out data analysis and manage
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support projects using GWAS, Mendelian Randomisation, and polygenic risk score analysis to uncover genetic mechanisms underlying complex traits. There are opportunities to integrate omics data across
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. By combining advanced AI recognition with live inventory monitoring, FabricFlow addresses long-standing inefficiencies in supply chains caused by manual processes, disconnected data, and lack
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by underrepresented groups Develop communication and education materials for local communities Apply/design appropriate analytical techniques to generate impactful insights. Formulate tailored
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of analytical philosophy Experience in research management and in leading project staff Experience abroad Willingness to acquire German language skills within 3 years that are sufficient for teaching in
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completion of a PhD in Psychology or a relevant area along with experience of collecting data from research participants. With excellent data analytic skills, you will be familiar with SPSS and at least one