Computational Science explores models of the natural and artificial world, through computer solutions of problems, which, due to their complexity, are intractable by conventional methods. Complex systems arise in a variety of fields, e.g. physics, biology, chemistry, eco-and other hybrid sciences, finance, socio-economic phenomena, and many others. In some cases, a formal model may be proposed and investigated; in others large amounts of data may be mined and empirical conclusions drawn or computational models may be designed and tested against available data. Key phases in Modelling/Analysing Complex Systems Modelling generally include some or all of the following:

• Development of the maths. model or data sourcing
• Development of the algorithm for numeric solution
• Implementation of the algorithm
• Generation of solutions e.g. through numerical simulation of the phenomenon of interest, data mining, matching and/or analysis
• Representation of the computed results or analyses
• Interpretation of results and model validation

Staff Members:

• Martin Crane
• Wu Hai
• Jane Horgan
• Gary Keogh
• Lynn Killen
• Aileen McLoughlin
• Heather Ruskin
• Alistair Sutherland
• Liam Tuohey

Postgraduate Members:

• Yaw Bimpeh
• John Burns
• Noreen Quinn
• Yongle Liu
• Niall McMahon
• Karl Podesta
• Atid Shamaie
• Saba Sharifi
• Ruili Wang
• Bao Zhu

Current Funded Projects:

• "Modelling the Immune Response to Viral Invasion" Funed by Enterprise Ireland.
• HEA PRTLI funding as the Biocomputation sub-group in the National Institute for Cellular Biotechnology.
• "Methodologies for yields in agr-sector applications." Funded by Teagasc

Affiliated Centres:

• National Institute for Cellular Biotechnology