[Elsnet-list] Working with Watson: Postdoc positions available at TrentoRise (Italy)
moschitti at disi.unitn.it
Wed Jun 6 11:37:02 CEST 2012
have you been fascinated by the IBM Watson system's achievements?
Have you ever thought that you could contribute to it?
If yes, there might be two great opportunities for this.
TWO Post-doctoral positions for 1 year (with the possibility of extension) are available
at the new IBM Language and Knowledge Center for Advanced Studies of TrentoRise - Italy
(TrentoRise is a joint research institution including the University of Trento, FBK and other important
research institutions of Trento)
Position 1: Information Extraction and Question Answering (Ref.Code IBM_PostDoc2012_IEQA)
This grant aims at developing a framework for Information Extraction and Retrieval based on deep
linguistic analysis. The main idea of the framework is to provide a flexible pipeline of NLP and IR
components, which can be used to model state-of-the-art systems, e.g., in the field of Question Answering.
Candidate Profile: the candidate must hold a PhD in Computer Science, e.g., Computational Linguistics,
Natural Language Processing, Information Retrieval or equivalent, and should be familiar with or
willing to learn the following topics:
- UIMA (Unstructured Information Management Architecture);
- Named Entity Recognition & Normalization / Concept Segmentation and Labeling
- Relation Extraction;
- Question interpretation, answer classification and extraction
- Dependency-based parsing, practice and theory
- Semantic role labeling
- Question analysis
- Search engine design
- Text Categorization/Filtering
- Document/passage ranking and re-ranking using different sources
- Indexing, search and retrieval for unstructured, semi-structured and structured data
- Retrieval models for Question Answering
- Relational models for sentence, paragraph and document representation
Programming skills are important whereas knowledge of the Italian language is not required.
Scientific tutors: Bernardo Magnini (FBK, Trento - http://hlt.fbk.eu/en/home)
Alessandro Moschitti (University of Trento - http://disi.unitn.it/moschitti/
Position 2: Machine Learning for NLP (Ref.Code IBM_PostDoc2012_LSM)
This grant aims at modeling and implementing a machine learning framework, which can be applied for
fast system prototyping. Kernel methods are seen as a viable approach to automatic feature engineering,
which is a severe bottleneck for the design of real-world applications. The other interesting problem that
will be studied concerns domain adaptation. Although the framework is supposed to be general, its
primary application domain will be natural language processing.
Candidate Profile: the candidate must hold a PhD in Computer Science, e.g., Machine Learning,
Computational Linguistics, Natural Language Processing, data mining or equivalent and should be
familiar with most of the following topics:
- Discriminative models, including Support Vector Machines and other Max-Margin approaches,
for tagging, extraction, (shallow) parsing and so on.
- Advanced data representation through Kernel Methods and Kernel Machines
- Online learning and Active Learning
- Supervised, Semi-Supervised, Unsupervised Learning, Domain Adaptation, Multi-task Learning in NLP
- Sequence labeling in NLP
- Relational Learning
- Graphical models, e.g., Conditional Random Fields and LDA, are a plus
Scientific tutor: Alessandro Moschitti (University of Trento - http://disi.unitn.it/moschitti/
Prof. Moschitti has been collaborating with the IBM Watson team since 2009. He has received two awards
from IBM. The postdocs (if successful in their research) will have the possibility to collaborate with
the IBM Watson team and carrying out working stage at IBM Watson of NY.
You can directly apply to
If you want to know more, please send an email to
moschitti at disi.unitn.it
salary net per month: 2k euros
The deadline for applications is June 30, 2012
(please note that the deadline indicated in the website above is going to be updated).
Your curriculum must show an appropriate record of publications in the areas of ML, NLP/CL or IR.
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