jonathan lawry asuwexbristolacuk phd studentship joint university bristol proudman oceanographic laboratory topic applying rulebased models sea level forecasting supervisors jonathan lawry university bristol kevin horsburgh proudman oceanographic laboratory project details storm surges response sea surface meteorological forces wind atmospheric pressure represent important component total sea level subject much scientific investigation marine scientists engineers need understand statistics surge occurrence tidesurge interaction long timescales order provide estimates extreme sea level design purposes numerical models tides surges based hydrodynamic equations long history coastal flood warning whilst models successful form backbone current operational forecast procedures inherently limited inaccuracies bathymetry meteorological forcing parameterisations subgrid scale processes real opportunities alternative datadriven methods surge prediction using artificial intelligence ai particular rulebased models rbms rbms provide highlevel linguistic representation mapping input output variables prediction problem allowing understandable models give insight important underlying relationships models also extended incorporate fuzzy probabilistic uncertainty typically present hydrology oceanography applications phd allow student demonstrate use rulebased models important environmental problem student develop rulebased models combining probabilistic fuzzy uncertainty compare deterministic forecasting techniques alternative time series methods student apply extend techniques lid3 algorithm learning probability estimation trees incorporating fuzzy description labels new models applied improvement tidal forecasts regions extreme tidal range prediction storm surges key sites around uk models also used examine principal physical causes extreme sea level events logical rules governing tidesurge interaction finally project demonstrate possibility using artificial intelligence interpretation ensemble forecasts successful applicant good numerical degree mathematics statistics computer science engineering physical science knowledge artificial intelligence probability theory fuzzy logic would advantage although full training given student provided training coastal oceanography numerical modelling necessary data tide gauges deterministic numerical models supplied pol met office funding project funded epsrc flood risk management consortium position open anyone noneu candidates must find alternative means fund difference uk overseas tuition fees contact please contact either jonathan lawry kevin horsburgh directly informal inquires closing date applications 30 june application forms available online applications references sent directly emma weeks dept engineering maths university bristol bristol bs8 1tr uk fri 08 aug 2008 015641 0100 uai phd studentship ai techniques flood prediction university bristol