Post-doc position available to work in Crepaldi’s lab at SISSA

Job title: ERC-funded Postdoctoral Research Fellowship in Statistical Learning and Reading
Location: Trieste, Italy
Salary: 33720E/year, gross
Hours: full time
Contract duration: 2+2 years
Application closes: 18/10/2016, 1pm Italy time
Preferred starting date: 1/12/2016, but negotiations are possible for later starting dates
Link to the official call:

We are seeking a highly motivated Postdoc for a 2-year position (renewable for another 2 years) in the Neuroscience Area at the International School for Advanced Studies (SISSA), Trieste, Italy.
The postdoc position is created as part of the ERC Starting Grant “STATLEARN – The reading brain as a statistical learning machine” (an abstract of the project is reported below). The project is highly interdisciplinary, and involves behavioural, ERP, fMRI/MEG and computational work. The post holder will be involved in one or more of these areas according to her/his skills and interests. Candidates with experience in any of the methods above are encouraged to apply; however, this position is particularly aimed at individuals with experience/skills in neuroimaging (other positions will be opened soon, more tight to the other profiles).
Candidates are expected to have a PhD in the field of Psychology or Cognitive Neuroscience, and a solid publication record. Experience in the domain of reading and/or statistical learning would be great for this position; however, this is not absolutely necessary, so people with a background in other fields are also welcome to apply. Good programming skills are required, as well as a good attitude toward teamwork.
This post is in the context of the Cognitive Neuroscience group at SISSA, a diverse, vibrant research group that covers perception, language, motion and abstract cognition; and is incredibly wide as far as the research approaches adopted — we do human and animal research; investigate healthy adults, elderly, kids, brain-damaged individuals, and blind people; and carry out computational as well as experimental research using eye tracking, electrophysiology, imaging, and TMS. A comprehensive description of Cognitive Neuroscience at SISSA can be found at
The formal application process is described in the official call at Interested candidates, however, are encouraged to contact the PI at

Abstract of the project STATLEARN:
Despite written language is not part of our genetic endowment, literate adults process an impressive amount of information as they read, and do that extremely flawlessly and nearly error-free. How this happens is largely unknown, and represents a fundamental issue for theories of human learning. Building on data from nonhuman primates, human infants and psycholinguistic experiments on word internal structure, STATLEARN tests the hypothesis that one fundamental cognitive mechanism underlies visual word identification, i.e., statistical learning. Human infants learn to chunk smaller perceptual units (e.g., oriented lines) into larger, meaningful objects (e.g., tools, faces), taking advantage of recurrent patterns in their distribution. As developing readers, they would apply this very same mechanisms to a newly–encountered type of visual objects, i.e., letters. On this basis, they would build progressively higher–order orthographic units, which eventually make their visual word identification as adult readers astonishingly efficient.
The project is composed of four work packages. One aims at identifying which principle(s) drive(s) statistical learning, and contrasts overall frequency, contextual diversity, and letter transitional probabilities. Because these factors co–vary in real languages, a second work package will involve adult readers in learning artificial languages, where we will build in any statistical properties we might need to test. A third package will seek signs of statistical learning directly into the performance of developing readers. A fourth package will assess positional constraints in the identification of morphemes (e.g., kind and ness in kindness). These work packages include behavioural, eye tracking, ERP, MEG and fMRI work. Bringing together evidence from such a wide array of approaches will allow to understand how statistical learning unfolds, and what kind of representations it brings into the human reading system.