Machine learning is becoming an essential part of a physicist’s toolkit. How should new students learn to use it? When Radha Mastandrea started her undergraduate physics program at MIT in 2015, she ...

A physics-informed deep learning model, PBCNet, is proposed for predicting the relative binding affinity of ligands in order to improve guiding structure-based drug lead optimization.

Machine learning developments for particle physics discoveries Research Group: Particle Physics Research Centre Number of Students: 1- Length of Study in Years: 4 Years Full-time Project: yes Funding ...

A research team has used a machine learning approach to investigate the evolution of shell structure for nuclei far from the stability valley. The study, published in Physics Letters B and ...

Another advantage of modeling diffusion using kinosons and machine learning is that it is significantly faster than calculating long-timescale, whole trajectories.

Project Description The Large Hadron Collider is the world’s highest energy collider reaching collisions at 13 TeV. The high energy allows us to search this unexplored region for signals of new ...

This year's Nobel Prize in Physics goes to two researchers who have made machine learning with artificial neural networks possible.

Machine learning projects have the potential to help us navigate our most significant risks — including wildfires, climate change, pandemics, and child abuse.

In addition to its structured curriculum, the top rated Advanced Machine Learning Course at Interview Kickstart also includes real-world capstone projects that simulate practical AI/ML challenges ...