[visionlist] Postdoc position in: Biological neural-network signal encoding using Artificial Intelligence

[Enver Sangineto]

Postdoc position in: Biological neural-network signal encoding using 
Artificial Intelligence


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Location: University of Trento, Italy
Duration: One year (it may be renewed for a second year).
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Project description:

Applications are invited for one full-time postdoctoral research 
position funded by the Brandy project 
(https://projects.unitn.it/brandy/) and in collaboration with the ERC 
project Backup (https://r1.unitn.it/back-up/). The goal of both projects 
is to investigate the brain network functioning using an 
inter-disciplinary approach. We are currently seeking a highly motivated 
Postdoc with a strong research record in Artificial Intelligence areas 
such as: Audio Processing, Signal Processing, Speech Recognition, 
Computer Vision, etc.
The successful candidate will participate in proposing, implementing and 
applying Deep Learning methods for the analysis and the interpretation 
of electrophysiological signals of biological neuronal networks. 
Specifically, we will use a Multi-Electrode Array (MEA) to access the 
activity of a culture of in-vitro alive neurons. The MEA response is a 
set of time-varying signals, one signal per electrode, usually 
characterized by a very low Signal-to-Noise-Ratio (SNR). These raw 
signals (possibly pre-processed to increase the SNR) will be used to 
train an artificial neural network in order to predict (“encode”) the 
biological neuron activity in the near future. Predicting the future 
activity of the biological neurons will show that the artificial network 
can “understand” and internally represent the “semantics” of the input 
MEA signal. We will investigate Deep Learning methods for brain 
(neuronal network) activity encoding and we will generalize the proposed 
methodology to different kinds of brain activity signals (e.g., fMRI, 
MEG, etc.).

The successful candidate will work in the NL lab of the Physics 
department of the University of Trento, supervised by Dr. Enver 
Sangineto and in collaboration with both the Artificial Intelligence 
group of Prof. Nicu Sebe (http://mhug.disi.unitn.it/ ) and the 
Nanoscience Laboratory of Prof. Lorenzo Pavesi 
(http://nanolab.physics.unitn.it/), in a strongly inter-disciplinary 
group composed of Computer Scientists, Biologists and Physicists.

For more information, please contact Dr. Enver Sangineto 
(enver.sangineto at unitn.it).

Interested applicants should send:

1. a one-page cover letter summarizing their interests, how they can 
contribute to the project, as well as the earliest date that they can 
start the appointment,
2. curriculum vitae, and
3. contact information for two references

to: enver.sangineto at unitn.it. Only applicants considered for employment 
will be contacted. Applications are reviewed on a rolling basis.