Connectionists: PhD Student position at the interface of neuroscience and machine learning

[Rava A. da Silveira]

The lab of Rava Azeredo da Silveira invites applications for a *PhD Student
position* at the *interface of computational neuroscience and machine
learning*, at the Institute of Molecular and Clinical Ophthalmology Basel
(IOB) in Switzerland, an associated institute of the University of Basel.

Research projects will involve modeling and data analysis; some projects
will be carried out in the framework of collaborations with experimental
labs. Questions will be chosen from a range of topics involving neural
representations as relating to behavior, neural coding, dynamics and
learning.

Candidates with backgrounds in mathematics, statistics, artificial
intelligence, physics, computer science, and engineering are welcome.
Experience with data analysis and proficiency with numerical methods, in
addition to familiarity with mathematical and statistical methods, are
desirable. Equally desirable are a spirit of intellectual adventure,
eagerness, and drive.

The positions will come with highly competitive work conditions and
salaries.

*Application deadline:*

For full consideration, please apply by 15 March 2021.

*How to apply:*

Please send the following information *in one single PDF, to *
*silveira at iob.ch* <silveira at iob.ch>*:*

1.     letter on motivation and interests;

2.     curriculum vitæ;

3.     transcripts of undergraduate and Master grades.

In addition, please arrange for three letters of recommendations to be sent
to the same email address. In all email correspondence, please include the
mention “APPLICATION-PHD” in the subject header, otherwise the application
will *not* be considered.

***

*The Silveira** Lab* focuses on a range of topics, which, however, are tied
together through a central question: How does the brain represent and
manipulate information?

Among the more concrete approaches to this question, the lab analyses and
models neural activity in circuits that can be identified, recorded from,
and perturbed experimentally, such as visual neural circuits in the retina
and the cortex. Establishing links between physiological specificity and
the structure of neural activity yields an understanding of circuits as
building blocks of cerebral information processing. On a more abstract
level, the lab investigates the representation of information in
populations of neurons, from a statistical and algorithmic—rather than
mechanistic—point of view, through theories of coding and data analyses.
These studies aim at understanding the statistical nature of
high-dimensional neural activity in different conditions, and how this
serves to encode and process information from the sensory world.

In the context of cognitive studies, the lab investigates mental processes
such as inference, learning, and decision-making, through both theoretical
developments and behavioral experiments. A particular focus is the study of
neural constraints and limitations and, further, their impact on mental
processes. Neural limitations impinge on the structure and variability of
mental representations, which in turn inform the cognitive algorithms that
produce behavior. The lab explores the nature of neural limitations, mental
representations, and cognitive algorithms, and their interrelations.
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