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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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instrumentation often located at synchrotron facilities. We have recently developed operando cells which can perform XPS measurements at > 1 bar pressure by using graphene membranes as X-ray and electron
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instrumentation often located at synchrotron facilities. We have recently developed operando cells which can perform XPS measurements at > 1 bar pressure by using graphene membranes as X-ray and electron
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. Developing an analytical framework to achieve the grant objectives, the postholder will also write and publish scientific papers on the relationship abiotic environmental variables and extinction selectivity
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. 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 and skills of the successful
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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
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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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. Concurrently, you will develop lower order analytical models and perform high fidelity computational simulations to corroborate experimental findings and propose other configurations to be subsequently
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replication. This post is fixed term for 3 years. What are you going to do? In this fully-funded project, you will: • develop and employ novel advanced biophysical instrumentation based on optical
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of mixed phase numerical or analytical flow modelling for icing. Experience conducting and analysing experimental data is desirable. You should have a record of academic publications in the field and be able