One research grant(s)  Bolsa de Investigacão - Mestre is now available in project SARRROCA, “Synthetic Aperture Radar Robust Reconfigurable Optimized Computing Architecture” - ref: PTDC/EEI-HAC/31819/2017 funded by FCT/MCTES through national funds, and, where appropriate, POCI - Programa Operacional Competitividade e Internacionalização e PORLisboa – Programa Operacional Regional de Lisboa, under the following conditions:
Duration
Six months from 1 of October 2019. The grant may be renewed within the period of the project, not exceeding the maximum period set by FCT for such grants. Renewal is subject to suitable performance
Legislation
A fellowship contract will be celebrated according to the Regulations for Research Grants of the Foundation for Science and Technology in force the INESC ID Grant regulations approved by FCT, and to the Status of Scientific Research Fellow (Lei nº 40/2004 de 18 de Agosto, and its successive amendments).
Monthly amount
The monthly amount of the grant 989,70€ is in accordance with the values stipulated in the “Regulations for Research Grants of the Foundation for Science and Technology” in force (https://www.fct.pt/apoios/bolsas/regulamentos.phtml.pt) and (http://www.fct.pt/apoios/bolsas/valores) and INESC-ID Lisboa Grant Regulations approved by FCT, and shall be rendered through a monthly bank transfer to an account held by the grantee
Objectives / summary
This research work aims to develop a fault-tolerant embedded system using Xilinx FPGAs. The research work will involve designing novel hardware architectures and study and implementation of adequate fault-tolerant mechanisms. Expected outputs of this work are the implementation of prototypes for real-time fault-tolerant architectures.
Required education Level and research experience
The candidate should have an MSc degree in Computer Science and Engineering, or Electrical and Electronic Engineering and research project experience in System on a Chip design using FPGAs from Xilinx.  The candidate should be familiarized with most common fault-tolerance mechanisms on FPGAs. Language skills - proficiency in English. Preference will be given to candidates willing to pursue a PhD degree.

One research grant(s)  Bolsa de Investigacão - Mestre is now available in project SARRROCA, “Synthetic Aperture Radar Robust Reconfigurable Optimized Computing Architecture” - ref: PTDC/EEI-HAC/31819/2017 funded by FCT/MCTES through national funds, and, where appropriate, POCI - Programa Operacional Competitividade e Internacionalização e PORLisboa – Programa Operacional Regional de Lisboa, under the following conditions:
Duration
Six months from 1 of October 2019. The grant may be renewed within the period of the project, not exceeding the maximum period set by FCT for such grants. Renewal is subject to suitable performance
Legislation
A fellowship contract will be celebrated according to the Regulations for Research Grants of the Foundation for Science and Technology in force the INESC ID Grant regulations approved by FCT, and to the Status of Scientific Research Fellow (Lei nº 40/2004 de 18 de Agosto, and its successive amendments).
Monthly amount
The monthly amount of the grant 989,70€ is in accordance with the values stipulated in the “Regulations for Research Grants of the Foundation for Science and Technology” in force (https://www.fct.pt/apoios/bolsas/regulamentos.phtml.pt) and (http://www.fct.pt/apoios/bolsas/valores) and INESC-ID Lisboa Grant Regulations approved by FCT, and shall be rendered through a monthly bank transfer to an account held by the grantee
Objectives / summary
This research work aims to develop a fault-tolerant embedded system using ARM/AVR/Arduino devices. The research work will involve designing novel hardware architectures and study and implementation of adequate fault-tolerant mechanisms. Expected outputs of this work are the implementation of prototypes for real-time fault-tolerant architectures.
Required education Level and research experience
The candidate should have an MSc degree in Computer Science and Engineering, or Electrical and Electronic Engineering and research project experience in embedded systems.  The candidate should be familiarized with Arduino and ARM platforms. The candidate should also demonstrate experience in sensors and actuators connected to an Arduino. Language skills - proficiency in English.  Preference will be given to candidates willing to pursue a PhD degree

Natural language processing (NLP) received a strong push in the last decade, due to the abundance of web data, and leveraging advances on statistical machine learning. While many different NLP tasks have seen significant progress, issues like (a) handling figurative devices (e.g., irony or metaphor) in written text, or (b) document-level parsing of discourse and/or argumentation structures, remain significantly challenging.
Discourse parsing requires understanding the communicative/argumentative roles of sentences or parts of a document, as well as their relationships. For that, typical features related to words and simple syntax cues are clearly insufficient, and mechanisms like understanding interactivity, coherence and thematic development are required. These difficulties motivate additional research towards building better representations for the text, that can then be explored by machine learning methods.
Discourse parsing is closely related to argumentation mining from text, a challenge that only recently has come to the attention of NLP researchers. Many important challenges remain open, particularly if we consider text that are not essentially argumentative, related with existing heterogeneous argumentative styles. Argumentation involves a large and heterogeneous set of linguistic representations humans have at their disposal. Argumentation processes have been studied in diverse areas (e.g., philosophy, linguistics, or even artificial intelligence); on the other hand, NLP approaches for argumentation mining are relatively scarce in the literature, particularly for other languages besides English.
The Discourse Analysis and Argumentation Mining from Text Sources (DARGMINTS) project proposes to study NLP/IE techniques for addressing the task of argument mining from text, focusing on the Portuguese language (for which there is no relevant prior work), considering sources such as (a) news articles, (b) parliamentary debate transcriptions, and (c) discussions in specific social network profiles. The development of an NLP pipeline for the Portuguese language will be carried on, leveraging recent advances in the area. This NLP pipeline will support more advanced tasks, related to document-level parsing of discourse and to mining argument structures, to be aligned with argumentation schemes and discourse theories.
We also envision the development of new interactive visualizations for exploring argumentation patterns and processes. Such visualizations will be the basis for building appealing applications, that resort to different types of textual sources (news, parliamentary debate or on-line discussion archives), in areas related with media studies, political science or forensics (e.g. identifying opinion makers, detecting fallacies or radicalization processes), as well as applications specifically tailored for researchers in applied linguistics.
Within the DARGMINTS project, the researcher that is to be hired will broadly work on all the tasks that were considered in the project, although with a specific focus on deep learning methods for natural language processing, particularly addressing cross-language and transfer-learning methods for applications related to argumentation mining from text.