Schmidt Futures, a philanthropic initiative co-founded by former Google CEO and Chairman Eric ... [ ] Schmidt and his wife Wendy, is expanding its investment in artificial intelligence research. Schmidt Futures announced today that it was investing $148 million to fund the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship, a program of Schmidt Futures. With this newest funding, Schmidt Futures, a philanthropic initiative co-founded by former Google CEO and Chairman Eric Schmidt and his wife Wendy, has now committed a total of $400 million to support the development of artificial intelligence (AI) for scientific discovery for other advances in technology and engineering fields. According to the announcement, the new funding will initially support about 160 postdoctoral fellows at nine universities around the world to learn and apply AI methods to their research. The fellowship is expected to expand to more institutions and countries in the future.

In the new papers, biologists at the University of Washington School of Medicine show that machine learning can be used to create protein molecules much more accurately and quickly than previously possible. The scientists hope this advance will lead to many new vaccines, treatments, tools for carbon capture, and sustainable biomaterials. "Proteins are fundamental across biology, but we know that all the proteins found in every plant, animal, and microbe make up far less than one percent of what is possible. With these new software tools, researchers should be able to find solutions to long-standing challenges in medicine, energy, and technology," said senior author David Baker, professor of biochemistry at the University of Washington School of Medicine and recipient of a 2021 Breakthrough Prize in Life Sciences. Proteins are often referred to as the "building blocks of life" because they are essential for the structure and function of all living things.

HAI Fellowship Programs offer opportunities to explore topics, conduct research, and collaborate across disciplines related to AI technologies, applications, or impact. The Institute for Human-Centered Artificial Intelligence (HAI) offers a 2-quarter program for Stanford Graduate Students. The goal of this program is to encourage interdisciplinary research conversations, facilitate new collaborations, and grow the HAI community of graduate scholars who are working in the area of AI, broadly defined. HAI is seeking graduate students to participate in this program. We would like to ensure the cohort is well-rounded across disciplines.

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Nooshin Abbasi is a post-doctoral research fellow at Brigham and Women's Hospital, Harvard Medical School, and a former research fellow at the Montreal Neurological Institute, McGill University. Her research interests include brain imaging, evidence-based imaging, and bioinformatics, with a focus on applying machine learning tools to large clinical and imaging datasets. Michael Dohopolski is a PGY5 radiation oncology resident. He has worked with Dr. Wang and Dr. Jiang at UT Southwestern on machine learning based clinical decision-making support tools with an emphasis on single prediction uncertainty estimation. She is in the Department of Neurosurgery, University of Pennsylvania, and Division of Neurosurgery, Children's Hospital of Philadelphia.

We are currently looking to identify a Psychiatry Postdoctoral Fellow (PDF) to cover our Research Coordinator for the MIRA, Mental Health Virtual Assistant project for the period of 12 months (maternity leave) - starting July 1st, 2022 (somewhat negotiable). They will be working on a multi-disciplinary team, inclusive of 2 computing science Master students, 2 psychiatry Postdoctoral Fellows (one working hand-in-hand with this Fellow on the expansion of services to additional groups and provinces (supporting French language translation and service expansion to Quebec, among other responsibilities), and one supporting the expansion of services to children and youth), and 1 Indigenous studies PhD student, supporting the co-creation of MIRA with Indigenous communities. Funding is secured to fully support this position. The PDF would be first offered a 6-month contract, with the opportunity for another 6 month extension following a review. The PDF would be working under the supervision of Drs.

Eligible to be employed as a postdoctoral fellow is a person who has completed a doctoral degree, or a foreign degree that is considered comparable to a doctoral degree, in medical physics, physics, statistics, computer science, mathematics or a similar subject. This eligibility requirement must be met at the latest at the time when the employment decision is made. A very good command of written and spoken English is required. Previous experience of image processing and programming is also required. Since employment as a postdoctoral fellow constitutes a merit-based employment for junior researchers, we primarily target those who have a doctoral degree that is not older than three years from the last application date.

Postdoctoral positions are appointed primarily for purposes of research. Applicants are expected to hold a Swedish doctoral degree or an equivalent degree from another country. In the first instance, a person who has completed a Swedish doctoral degree in Law with a focus on IT Law and AI or has a foreign degree that is deemed to correspond to this or has achieved equivalent scientific competence is sought, no more than three years before the application deadline. If there are special circumstances, the doctoral degree may have been completed earlier. Such reasons are leave due to illness, parental leave, clinical service, elected positions within trade unions or other similar circumstances.

Algorithm outperformed thoracic radiologists in detecting COVID-19 in new study. Northwestern University researchers have developed a new artificial intelligence (A.I.) platform that detects COVID-19 by analyzing X-ray images of the lungs. Called DeepCOVID-XR, the machine-learning algorithm outperformed a team of specialized thoracic radiologists -- spotting COVID-19 in X-rays about 10 times faster and 1-6% more accurately. The researchers believe physicians could use the A.I. system to rapidly screen patients who are admitted into hospitals for reasons other than COVID-19. Faster, earlier detection of the highly contagious virus could potentially protect health care workers and other patients by triggering the positive patient to isolate sooner.

IMAGE: Generated heatmaps appropriately highlighted abnormalities in the lung fields in those images accurately labeled as COVID-19 positive (A-C) in contrast to images which were accurately labeled as negative for COVID-19... view more Called DeepCOVID-XR, the machine-learning algorithm outperformed a team of specialized thoracic radiologists -- spotting COVID-19 in X-rays about 10 times faster and 1-6% more accurately. The researchers believe physicians could use the A.I. system to rapidly screen patients who are admitted into hospitals for reasons other than COVID-19. Faster, earlier detection of the highly contagious virus could potentially protect health care workers and other patients by triggering the positive patient to isolate sooner. The study's authors also believe the algorithm could potentially flag patients for isolation and testing who are not otherwise under investigation for COVID-19. The study will be published on Nov. 24 in the journal Radiology.