Concepts of Machine Learning and neuroscience are closely related to each other because artificial neural networks of artificial intelligence are made with the concept of the neurons of the human brain. Neural networks mostly perform supervised learning. To master image recognition, a database of more than 14 million photographs of objects that have been categorized and annotated by people. The networks develop a statistical understanding of what images with the same label have in common. When shown a new image, the networks examine it for similar numerical attributes. If they find a match, they will recognize it as the same category.

Scientists can examine how the system generates its output and then make inferences about how the brain does the same thing. This approach can be applied to any cognitive task of interest to neuroscientists, including processing an image. This collaboration brings in the job scopes for neuroscientists who are also familiar with data analytics.

There are several jobs available for machine learning neuroscience. These jobs focus on the building of a system that would reproduce brain data or a system that would analyze the long array of neurological data. These jobs are mostly available in the medical field. There are also several designations in the research field.

Neuroscientists are still a long way from understanding how the brain goes about a task such as distinguishing jazz from rock music, but machine learning does give them a way of constructing models with which to explore such questions. If researchers can design systems that perform similarly to the brain their design can inform ideas about how the brain solves such tasks.

Some of such jobs are :

• ML engineer in neuroscience at BrainSightAI
• ML engineer in neuroscience at Mad Street Den