Doctoral Student Position at the Interface of Neuroscience and Machine Learning

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 / Contact

Please send the following information in one single PDF, to silveira@iob.ch :

• Letter on motivation and interests;
• Curriculum vitæ;
• 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.

IOB is a research institute combining basic and clinical research. Its mission is to drive innovations in understanding vision and its diseases and develop new therapies for vision loss. IOB is an equal-opportunity employer with family-friendly work policies.

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.