Trosolwg
Cyfrifo nodweddion optegol ac electronig lled-ddargludyddion
Modelu dyfeisiau lled-ddargludol
Efelychiad o geulo gwaed
Dotiau cwantwm coloidaidd
Cytometreg Llif
Cyfrifo nodweddion optegol ac electronig lled-ddargludyddion
Modelu dyfeisiau lled-ddargludol
Efelychiad o geulo gwaed
Dotiau cwantwm coloidaidd
Cytometreg Llif
Gwobr Sabothol Cydweithredu Rhyngwladol ESPRC (Cyngor Ymchwil Peirianneg a Gwyddorau Ffisegol)
Cyfle i gydweithio gyda'r Athro Anne Carpenter o Sefydliad Broad MIT a Harvard, Amnis Corporation, Cancer Research UK a'r Ganolfan Nanoiechyd yn Abertawe.
Nod y fenter newydd hon yw meithrin cydweithio rhyngwladol hirdymor rhwng ymchwilwyr blaenllaw yn y DU a'u cyfoedion rhyngwladol a chwalu rhai o'r rhwystrau i gydweithio rhyngwladol estynedig. Mae'r rhaglen yn caniatáu i ymchwilwyr yn y DU ymweld â chanolfannau rhagoriaeth tramor am rhwng 6 a 12 mis, wedi'i hadeiladu o gwmpas agenda ymchwil o ansawdd uchel. Mae'r cydweithio rhyngwladol presennol wedi troi o amgylch prosiectau tymor byr, cynadleddau ac ati a theimlwyd y byddai cydweithredu proffil uwch tymor hwy yn fwy buddiol.
This module offers a balanced, streamlined one-semester introduction to Engineering Statistics that emphasizes the statistical tools most needed by practicing engineers. Using real engineering problems students see how statistics fits within the methods of engineering problem solving and learn how to apply statistical methodologies to their field of study. The module teaches students how to think like an engineer when analysing real data. Mini projects, tailored to each engineering discipline, are intended to simulate problems that students will encounter professionally during their future careers. Emphasis is placed on the use of statistical software for tackling engineering problems that require the use of statistics.
The aim of this module is to introduce the science of measurement and explain the potential and the limitations of sensors commonly used in performance sports applications. Throughout the module, foundational principles will be explained using sporting examples of data analysis, with a particular focus on time-series data. A core principle of the module is that the process of measurement must be understood before applied studies are designed and data analysis is undertaken. The limits to measurement and the errors that can exist in a dataset have to be appreciated in the context of performance sport applications. The origin of the data also has to be considered as there are often hidden assumptions influencing its acquisition and pre-processing built into sensors. The aim here is to educate students about where their data comes from and to encourage them to critically assess the conditions under which valid measurements can be obtained in applied performance environments.
Students will carry out a medical engineering design project in groups of up to 6 people.