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  1. Hace 1 día · Job description. Cervical cancer is the most common gynecological cancer and this cancer could be prevented by screening programs. Currently the participation rates are low and in this project we want to increase the efficiency and effectiveness of current screening approaches by lowering participation barriers using innovative technologies.

  2. Hace 1 día · The University of California (UC) is a public land-grant research university system in the U.S. state of California.Headquartered in Oakland, the system is composed of its ten campuses at Berkeley, Davis, Irvine, Los Angeles, Merced, Riverside, San Diego, San Francisco, Santa Barbara, and Santa Cruz, along with numerous research centers and academic abroad centers.

  3. Book cheap flights from Turku with Europe's greenest ultra-low cost airline. Find the best deals with our fare finder & flight bundles.

  4. Hace 3 días · Our staff are vital in creating the University’s academic and professional community, and we value working collaboratively with others within our global network of contacts in business, the professions, the public, and the voluntary sectors. Find out what it's like to work at Bath.

  5. Hace 2 días · The university of campinas is a public university funded by the state of são paulo, brazil. Established in 1966 in the city of campinas, with the g ... Deciphering the Gut’s Clues to Our Health University of Turku 5 min read Understanding Users to Optimise 3D Experiences Centrum Wiskunde & Informatica ...

  6. Hace 5 días · The University of Liverpool (abbreviated UOL) is a public research university in Liverpool, England. Founded as a college in 1881, it gained its Royal Charter in 1903 with the ability to award degrees, and is also known to be one of the six 'red brick' civic universities, the first to be referred to as The Original Red Brick.

  7. Hace 1 día · Abstract. Background: The reproducibility of machine-learning models in prostate cancer detection across different MRI vendors remains a significant challenge. Methods: This study investigates Support Vector Machines (SVM) and Random Forest (RF) models trained on radiomic features extracted from T2-weighted MRI images using Pyradiomics and MRCradiomics libraries.