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Machine Learning, Human-Computing Interactions, Social Sciences, and Public Health. Applicants should hold, or be close to completion of, PhD/DPhil with research experience in computer science, statistics
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Postdoctoral Researcher in Machine Learning of Isomerization in Porous Molecular Framework Materials
Experience in uncertainty quantification or statistics applied to quantum chemistry and machine learning would be advantageous For more details, please take a look at the role profile. We'll still consider
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collaboration with statistical physicists for data analysis and experimental design. The Associate is expected to generate breakthrough ideas in the assigned area of research, as well as to carry out research in
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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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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