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theoretical understanding of statistical machine learning methods relevant to the project: Bayesian learning, machine learning, spiking neural networks. Experience of programming (e.g. with Python) and data
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mass spectrometry (especially GC-MS) and programming (e.g. R), statistical knowledge for omic-scale research questions Scientific publishing experience in renowned, subject-relevant peer-reviewed
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sustainability, performance, and reliability. Our research leverages optimization techniques, applied machine learning, and statistical analysis to achieve these objectives. Through the DecAI project we will work
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students and postdoctoral researchers. Key responsibilities will include: Pre-registering data analysis plans; Leading and conducting advanced statistical analyses (e.g., twin/family designs, genomic and
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techniques. Analyse experimental data using statistical tools and computational methods. Collaboration & Mentorship: Collaborate with interdisciplinary teams of researchers and students. Mentor graduate and
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interests include flow of soft materials, non-equilibrium dynamics, dynamics of soft glasses, statistical physics of yielding, shear thickening of dense suspensions, phase behaviour, self-assembly, fluid
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genetics consortia. To be considered for the role you will hold, or be close to completion of, a PhD/DPhil in in epidemiology, genetic epidemiology, molecular epidemiology, medical statistics or other
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expertise in statistics, mathematics, engineering and AI with industry scientists. Within the partnership, small research teams will focus on ambitious, ‘blue sky’ research for novel methods development
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hundreds of samples from multiple independent marine transmissible cancer clones. The role provides an exciting opportunity to combine single-cell cancer genomics with molecular cytogenetics and statistical
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conservation of marine and coastal ecosystems. The successful candidate will also require strong technical skills in terms of statistical and spatial analyses, and the ability to coordinate and manage large