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opportunity to work within a multidisciplinary team that includes world experts in psychology, clinical neuroscience, statistics, patient-clinician communication, and cancer survivorship care. The post-holder
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data independently. The post holder must also have a strong statistical background, with at least one recent publication in an internationally reputable journal. Application Process You will be required
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sleep; performing anatomical tract tracing; analysing existing and new datasets using python and Matlab using advanced statistical methods such as machine learning; collaborating with other members
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at the intersection of these research areas. You should hold, or be close to completing, a PhD/DPhil in mathematics, statistics, physics, engineering, data science, or a related field. Experience in cancer
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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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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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techniques. Analyse experimental data using statistical tools and computational methods. Collaboration & Mentorship: Collaborate with interdisciplinary teams of researchers and students. Mentor graduate and