[Corpora-List] University of Trento: Post-doc and research professor positions available

[Marco Baroni]

The CLIC laboratory of the Center for Mind/Brain Sciences (CIMeC) of
the University of Trento announces the availability of:

TWO 2-YEAR POST-DOC POSITIONS

- one post-doc position in the computational neuroscience of language

- one post-doc position in the construction of multimodal semantic
   spaces (partially founded by a Google Research Award)

Read below for details.

The Center for Mind/Brain Sciences (CIMeC) is also offering a number
of attractive

FIXED-TERM RESEARCH PROFESSOR POSITIONS

for advanced post-doctoral researchers with an interest in any area of
the cognitive (neuro)sciences, including language and computational
models.


* Research environment *

The Language Interaction and Computation lab (clic.cimec.unitn.it) is
a unit of the University of Trento's Center for Mind/Brain Sciences
(www.cimec.unitn.it) or CIMEC: an interdisciplinary center for the
research in brain and cognition including neuroscientists,
psychologists, (computational) linguists, computational
neuroscientists, and physicists. CLIC consists of researchers from the
Departments of Computer Science and Cognitive Science carrying out
research on a range of topics, including concept acquisition and
information extraction from very large multimodal corpora, combining
brain data and data from corpora to study cognition, and methods of
theoretical linguistics.


* Post-doc position in computational neuroscience of language *

A 2-year post-doctoral position in the computational neuroscience of
language with a focus on the organization of conceptual knowledge in
the brain will soon become available at CIMeC/CLIC.

The successful candidate will work as part of a larger project whose
objective is to combine empirical data of different types (corpus
co-occurrence patterns, elicitation experiments, neuroimaging data) to
arrive at a better understanding of the organization of conceptual
knowledge in the mind and brain. Your task will be to continue
on-going work which uses machine learning methods to extract
conceptual representations from recordings of neural activity (EEG,
MEG and fMRI).

The candidate should have technical knowledge of computational
linguistics and machine learning, and familiarity with theories of
ontologies and the lexicon.  Programming skills are a must, and
experience with neuroimaging techniques, experimental design
(elicitation/behavioural), machine learning and signal processing
would be a plus.


* Post-doc position in multimodal semantic spaces *

A 2-year post-doctoral position on multimodal semantic spaces is
available at CIMeC/CLIC. The scholarship is partially sponsored by a
Google Research Award, and the project will be carried out as a
collaboration between CLIC members and the Zurich Google Research
team.

The automated measurement of semantic similarity between
words/concepts through semantic space models such as Latent Semantic
Analysis or Topic Models has been a success story in text mining
(Turney and Pantel 2010). Today, through the Web, we have access to
huge amounts of documents that contain both text and images. While the
use of text to improve image labeling and retrieval is an active and
growing area of research (e.g., Wang et al. 2009), in this project we
go the other way around, and develop novel techniques to extract
multimodal semantic spaces from texts and images, in order to improve
the measurement of semantic similarity among words. A recent trend in
computer vision represents images as vectors that record the
occurrence, in the analyzed image, of a discrete vocabulary of "visual
words" (Yang et al. 2007). This development paves the way to the
integration of visual word cooccurrence features into the text-based
vectors of current semantic space models. The topic is expected to
have a strong impact both on the applied front, as a breakthrough in
the acquisition of large semantic repositories, and from a theoretical
point of view, leading to "embodied" models of computational learning
that are more directly comparable to what human learners do (Barsalou,
2008).

The successful candidate will have a strong computational background,
including familiarity with machine learning and/or statistical
methods, and have conducted research in either natural language
processing or computer vision. An interest in exploring the
connections between artificial and natural intelligence and cognition
is also desirable.


* How to apply *

For additional information please send an expression of interest (with
CV) to:

- computational neuroscience: Brian Murphy (brian.murphy at unitn.it)
- multimodal semantic spaces: Marco Baroni (marco.baroni at unitn.it)
- research professorships: Massimo Poesio (massimo.poesio at unitn.it)


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