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subject, such as psychology, biology, human genetics ii) an MSc in a relevant subject involving human genetic research or child development research iii) Prior experience working with statistical packages
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world-leading or internationally excellent in its quality. Every year Cranfield graduates the highest number of postgraduates in engineering and technology in the UK (Source: Higher Education Statistics
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and statistics knowledge Unix command line github Competences Strong abilities to work as part of a team Strong communication skills The Offer – Working Conditions Contract duration:4 year PhD student
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, Deep Learning and statistics is required. You must have good knowledge of English in both speech and writing and have the capacity to work independently as well as in teams. For further information about
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state-of-the-art research. You must have a master's degree in computer science or similar subject. Experience with machine learning, Deep Learning and statistics is required. You must have good knowledge
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impacts. The research will be quantitative and based on multiple data sources including field experiments and agricultural statistics. The PhD student will use and develop biophysical models of nutrient
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PhD Studentship: Data-driven Probabilistic Modelling of Clonal Dynamics in Human Tissues and Cancers
include: 1) statistical analysis of “big data” coming from clinical genomics, 2) stochastic modelling of clonal dynamics, 3) Bayesian inference from experimental cell lineage tracing and imaging data
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or equivalent experience in these topics and experience in field-work, modelling and statistics is desirable. A willingness and capacity to work independently and as part of team on LIPTON Tea Estates in Kenya
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usefulness of the forecast, and perception of forecast performance by the public. Statistical post-processing techniques can help to reduce forecast errors by training machine learning models on data sets
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the observable climate (𝑋) that varies significantly across the model ensemble, and which exhibits a statistically significant relationship, 𝑓, with variations in some important variable (𝑌) describing