These samples have been generously donated by UCSF students, postdocs, and alumni, in order to offer you ideas about how to present your own skills and experiences. We make no claims that they are perfect, but we offer them as useful examples. Do not copy the text exactly. To do so is considered plagiarism. It is to your advantage to create a document that stands out from your fellow applicants...

Overt exploration or sampling behaviors, such as whisking, sniffing, and saccadic eye movements, are often characterized by a rhythm. In addition, the electrophysiologically recorded theta or alpha phase predicts global detection performance. These two observations raise the intriguing possibility that covert selective attention samples from multiple stimuli rhythmically. To investigate this po...

Amethod wasdeveloped using liquid chromatography linked to atmospheric pressure ionization-tandem mass spectrometry (LC-MS-MS) for the measurement of the opiates, morphine, codeine, 6-monoacetylmorphine (6-MAM), acetylcodeine (AC), and heroin in oralfluid collected from patients attending a substance abuse clinic. Of the513oralfluid samples tested, 297 showed detectable concentrations of1 or mo...

These samples have been generously donated by UCSF students, postdocs, and alumni, in order to offer you ideas about how to present your own skills and experiences. We make no claims that they are perfect, but we offer them as useful examples. Do not copy the text exactly. To do so is considered plagiarism. It is to your advantage to create a document that stands out from your fellow applicants...

One of the most commonly used statistical techniques is the comparison of two independent samples of measurement data. More specifically, the comparison of the means of two independent samples. This is often the basis for making a decision to go with a particular method, process or supplier. The validity of the procedure hinges on the random selection of items for each sample. The correctness o...

We study the worst case error of kernel density estimates via subset approximation. A kernel density estimate of a distribution is the convolution of that distribution with a fixed kernel (e.g. Gaussian kernel). Given a subset (i.e. a point set) of the input distribution, we can compare the kernel density estimates of the input distribution with that of the subset and bound the worst case error...