© 2019, DISCnet            DISCnet is the Data Intensive Science Centre in SEPnet, and an STFC Centre for Doctoral Training;  a collaboration between

the Universities of Southampton, Sussex, Portsmouth, Queen Mary University of London, and Open University

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Skilled Data Scientists

PhD Students coming from DISCnet will have a high level of expertise in skills that industry needs; notably in data science and machine learning. By nature they are keen and natural problems solvers. Alumni work in a wide range of industries where they are key data scientists. Many wish to work on placement projects, as consultants or are seeking employment outside academia. Please contact Colin Hayhurst for further details, c.j.hayurst(at)sussex.ac.uk

Data Intensive Science Toolkit

Our students will be trained to be highly numerate and computationally skilled, with access to a vast “toolkit” of methods, and experience in applying these methods to real research problems. In particular, we have expertise across the following areas of data intensive science:

  • Raw data processing

  • Image analysis

  • Pattern recognition,

  • Object detection

  • Object classification

  • Machine learning for classification and regression

  • Application of statistics to research problems

  • Multi-Variate Analyses

  • Bayesian methodologies for model parameter estimation

  • Hierarchical probabilistic modelling

  • Model selection

  • Numerical simulation on massive scales

  • Data mining

  • HPC (algorithms, software/system management, cloud applications)

Our students will be trained to be highly numerate and computationally skilled, with access to a vast “toolkit” of methods, and experience in applying these methods to real research problems. In particular, we have expertise across the following areas of data intensive science:

  • Raw data processing

  • Image analysis

  • Pattern recognition

  • Object detection

  • Object classification

  • Machine learning for classification and regression

  • Application of statistics to research problems

  • Multi-Variate Analyses

  • Bayesian methodologies for model parameter estimation

  • Hierarchical probabilistic modelling

  • Model selection

  • Numerical simulation on massive scales

  • Data mining

  • HPC (algorithms, software/system management, cloud applications)