Pint of Science Aveiro 2022
Our group joined the Aveiro Pint of Science 2022, to talk about black holes... and have a pint!
Other News & Events
Black holes course for high school teachears and undergraduate students
Gr@v, together with the Portuguese Physics Society and the EPS Young Minds Aveiro (association of Physics students) is promoting a (non-technical) black holes course, online, for high school teachears and undergraduate students.
How do we find planets?
Check out Gr@v member Miguel Zilhão explaining "How do we find planets", an outreach video (in Portuguese).
UA coverage on the Stanford University ranking
Read the U. Aveiro coverage on the Stanford University researcher's ranking, led by John P. A. Ioannidis, that features several Gr@v members.
Discussion of the paper "Highly accurate protein structure prediction with AlphaFold" by J. Jumper et al.
Speaker
Felipe Freitas (UA)
Event date
Venue
Sala Sousa Pinto and online
Ends on
Event type
Highly accurate protein structure prediction with AlphaFold
J. Jumper et al.
Nature 596 (2021) 583–589
Group hike in the Paiva walkways
Great way to close the academic year with a sunny (and refreshing) group hike in the beautiful walkways next to Paiva river. Enjoy the holidays!
Black holes course for high school teachers
Introduction to computer vision with pyTorch
Gr@v member Felipe Freitas will lecture a free course on "Introduction to computer vision with pyTorch". Classes will take place on Fridays (the next class will be April 9th 2021) every two weeks, from 2:30 pm to 4:30 pm. More info below (in Portuguese).
Interpretable Machine Learning for Astrophysics, using Symbolic Regression and Graph Neural Networks
Speaker
Miles Cranmer (Princeton University)
Event date
Venue
online (only)
Event type
In this talk I will argue two points. 1) Symbolic regression, a machine learning technique that fits data by iteratively searching the space of all possible analytic equations, should be a standard machine learning algorithm in astrophysics. 2) Symbolic regression can be extended to high-dimensional spaces, such as to models for N-body simulations, using the method we have developed.