© 2020, 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  Gill Prosser,  Gill.Prosser@port.ac.uk, or Ian Sillett, I.M.Sillett@sussex.ac.uk, for further details.

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)