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. You will draw on ideas from Bayesian optimization and Bayesian deep learning, generative modelling, high throughput screening, and combinatorial synthetic chemistry. Responsibilities and qualifications
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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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for health policy decision-making, these methods will be developed using a Bayesian framework. This PhD project will deliver a substantial contribution to original research in the area of health data science
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are tracked and evaluated, providing detailed information about their materials, manufacturing processes, and lifecycle. With DPPs, parties through the supply chain (including consumers) can access reliable
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creative – You bring substantial knowledge of statistical (e.g. Bayesian) methods, strong analytical skills, and creativity. Programming skills – You are proficient in Python and/or Matlab. Research
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(DPPs) has become a regulatory priority. These passports are envisioned to revolutionize the way products are tracked and evaluated, providing detailed information about their materials, manufacturing
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processes related to carbon cycling in the soil-plant system Experience with Bayesian inference and machine learning is an asset Ability to work independently and cooperatively as part of an interdisciplinary
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system using deep learning (DL). The project’s objectives include generating training data from synthetic datasets and real-world images (cadaver and actual intraoperative THR images), developing a marker
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biomarkers of treatment success in the broader context of respiratory allergy.Key objectives include:• Evaluating allergic sensitization patterns in soldiers prior to deployment using an extended panel
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funding. Candidate Requirements The successful candidate will require a proven track record in research fieldwork and historical methodology. In its assessment, the selection committee will prioritise