SLICE - BI| 2021/198
The candidate will work on the project SLICE, which aims to develop and validate an authoring-friendly framework for the creation of transparent social intelligent agents capable of being deployed as virtual characters in training simulations or games or used to drive the behaviour of social robots. This framework joins two until now disparate areas of artificial intelligence research, namely, social agent architectures and machine learning. In particular, the candidate will work on the implementation in the agent framework FAtiMA of an authoring pipeline that allows agent creation by automatic interpretation and machine comprehension of narrative.

LAW-NLP aims to explore the use of natural language processing to help users understand and identify relevant Portuguese consumer laws that apply to their situation. To that end, the applicant will take part in an annotation task, aiming at enriching with highly granular linguistic information legal text from a specific domain, using an appropriate annotation tool. The linguistic information concerns semantic structuring of predicate-argument relations, correference, modification scope, as well as domain-specific named entities and relations.
The grantee is also expected to have an active role in a critical review of related work in view of adapting relevant contributions from different annotation schemes to the specific nature and goals of task at hand, including the domain-specific features of the texts, and the particular aspects of Portuguese syntax and semantics, aiming at producing a comprehensive review of the state of the art and scientific research papers on the annotation task and the analysis of the linguistic data.

IntelligentCare project develops a multimorbidity management decision support system focused on the patient's vision of value, i.e., achieving the outcomes that matter for patients. To this end, analytical methodologies to identify high risk multimorbidity patient groups, as well as to evaluate and define evidence-based clinical pathways associated with each patient group will be used. The project contributes to improved healthcare delivery through precision and personalized medicine and to contribute to market sustainability using new technologies. INESC-ID leads a project activity for identifying and and characterizing the main clusters of multimorbidity in clinical data. The applicant to be hired will join a team engaged in performing exploratory analysis of clinical data for clinical phenotyping and identification of most frequent representative groups iin multimorbidity, and investigation and exploration of different data mining algorithms supporting this task.

Exploration of novel SNP data representations for high-order epistasis detection with special focus on sparse computing and machine learning techniques. The key aspect of the research work will be investigating different alternative codifications, models and architectures for the representation and processing of SNP data, examining potential impacts in the time-to-solution and energy efficiency of epistasis search algorithms.