Researchers call for gender equality and career support for women in the workplace, and an end to “the doom and gloom narrative” over their limited numbers.
A collection of essays explores understandings of a vital bodily fluid in the period 1400-1700. Its contributors offer insight into both theory and practice during a period that saw the start of empiricism and an overturning of the folklore that governed early medicine.
Almost 30 years on from the discovery of the genetic defect that causes cystic fibrosis, treatment options are still limited and growing antibiotic resistance presents a grave threat. Now, a team of researchers from across Cambridge, in a major new centre supported by the Cystic Fibrosis Trust, hopes to turn fortunes around.
New research from the Faculty of Education lifts the lid on an influential academy school, and finds an authoritarian system that reproduces race and class inequalities.
The past few years has seen an explosion in the number of studies using organoids – so-called ‘mini organs’. While they can help scientists understand human biology and disease, some in the field have questioned their usefulness. But as the field matures, we could see their increasing use in personalised and regenerative medicine.
What connects a series of volcanic eruptions and severe summer cooling with a century of pandemics, human migration and the rise and fall of civilisations? Tree rings, says Ulf Büntgen, who leads Cambridge’s first dedicated tree-ring laboratory in the Department of Geography.
Police at the “front line” of difficult risk-based judgements are trialling an AI system trained by University of Cambridge criminologists to give guidance using the outcomes of five years of criminal histories.
Fairness, trust and transparency are qualities we usually associate with organisations or individuals. Today, these attributes might also apply to algorithms. As machine learning systems become more complex and pervasive, Cambridge researchers believe it’s time for new thinking about new technology.
Cambridge researchers are pioneering a form of machine learning that starts with only a little prior knowledge and continually learns from the world around it.