Innovative geographic knowledge discovery is becoming increasingly possible through the analysis of large-scale data, for instance made available by Earth observation projects leveraging satellites and remote-sensing, provided by ground-level sensors, or given as volunteered geographical information (e.g., geo-referenced multimedia contents posted online on social media platforms). However, problems that involve combining remotely-sensed data with volunteered geographical information are only now starting to be explored, and they still involve a number of practical challenges, e.g. related to appropriate content classification.
Over the recent years, data classification leveraging deep neural networks has also become increasingly popular. These methods have been reported to result in impressive performance gains, when applied to problems related to processing images or natural language. Within GIScience research, deep learning can also have several applications, that are only now starting to be explored (e.g., for improving land coverage analysis, spatial downscaling methods, and general classification tasks that involve combining different types of data).
Within the MIMU project (i.e., acronym for MIning MUlti-source and MUlti-modal geo-referenced information), the researcher that is to be hired will work on the use of machine learning approaches for the discovery and mapping of innovative geographic knowledge through the analysis and processing of large-scale volunteered data (e.g., geo-referenced multimedia contents such as images and textual descriptions, posted on social-media platforms like Flickr, Twitter, or Foursquare), in combination with more traditional sources (e.g., remote- sensing products available in the context of initiatives like ESA's Sentinel/Copernicus programme, and/or tables with socio-demographic data made available by statistical offices). The complex relations between the different types of information, as well as the temporal and geographical dimensions of the data, introduce new challenges that will explored throughout the project, in an attempt to go beyond the current state-of-the-art.
The expiration of the contract that will operate with the communication referred to in article 345 (1) of the Código do Trabalho (Labor Code), meaning that the employer shall notify the termination of the contract to the employee, at least 7, 30 or 60 days in advance, according to whether the contract lasted up to six months, six months to two years, or per longer period.

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.

The PhD Researcher (PhDR) to be hired for this position will support the project’s Principal Researcher, ensuring the  evolution of the scientific and development facets of the project. The PhDR will be tasked with the daily management of the project’s work team, guiding and coordinating the several developments foreseen in the project’s several Activities, including the (co-)supervising of MSc students, in this context. S/He will also serve as main point of contact between the different partner’s technical teams. Although high-level scientific and strategic decisions will be the responsibility of the Principal Researcher, the PhDR will lend his/her support in the definition and implementation of such decisions. Finally, it will be the PhDR’s responsibility to develop and implement the technological solutions needed for the project.

international selection call for one doctorate position under the programme SAICT-45-2017 PTDC/EEI-EEE/32550/2017 - Smart Transformers for Sustainable Grids – funded by Fundação para a Ciência e a Tecnologia, in the form of an employment contract under an unspecified fixed-term work contract – in the framework of Decree-Law No. 57/2016, of August 29, regulations for hiring doctorates to stimulate scientific and technological employment in all areas of knowledge - RJEC), with the amendments introduced by Law No. 57 / 2017, dated July 19, also taking into account the provisions of Regulatory Decree No. 11-A / 2017, of December 29 and the Código do Trabalho  (Labor Code), approved by Law No. 7/2009, of February 12, in its current wording -  being the basis of the contracting the performance of a specific service, precisely defined and non-durable, with a view to performing the following functions:
- Computer implementation of theoretical models for application to the project. Controllers design, development of dedicated simulation programs, participation in the construction of a prototype and experimental validation;
- To work in close coordination with a research team dedicated to the project. Participate in all stages of the research project, proposing innovative solutions, accompanying the research team and promoting the scientific dissemination of the developed work;
- In collaboration with the research team, promote and prepare at least one scientific publication per year in 1st Quartile Scimago / Web of Science journals;
- Promote and prepare the submission of patents;
- Participate in the project meetings and in all the dissemination actions carried out within the scope of the project;

GROUP LEADER
Prof.Rui Maranhão Abreu
rui@computer.org
RESEARCH PROJECT/RESEARCH GROUP
Information and Decision Support Systems Group Website
https://idss.inesc-id.pt/
POSITION DESCRIPTION
-Research Project / Research Group Description:
It is well-known that Quantum Computing (QC) has the potential to solve complex problems efficiently in various domains and bring breakthroughs in science and technology. Nowadays, quantum applications span over algorithms addressing optimization problems, such as radiotherapy optimization, machine learning techniques, chemistry simulations, and modelling (eg., handling uncertainties when predicting events).  The development of QC is also driven by the urgent need of solving ever-complex and large-scale problems, which current (super)computers cannot solve, and QC comes right on time to bring revolutionary computational power to handle such complexity. 
Though QC hardware is still immature, as most of the existing QC systems can only handle a limited number of qubits and are affected by noise, this is the time for getting “quantum ready”.  This means that industrial and academic research institutions can use this time to learn QC, devise new quantum algorithms, and build QC communities.  Specifically, the Quantum for Software Engineering (Q4SE) project aims to establish the theoretical foundations of a QC infrastructure supporting all aspects of software engineering and enable the development of high-quality quantum applications that can assist developers throughout the entire development lifecycle.   
We regard Q4SE’s domain as a new sub-field of software engineering that we expect to see growing in the next years, levering quantum languages and frameworks, such as Q# and Qiskit, Consequently, Q4SE will become highly relevant for the software engineering community in the years to come. Currently, there is still no one leading any efforts on Quantum Computing in the Programming Languages and Software Engineering communities. 
-Job position description:
The ambition of this project is to develop radically new methods for automated program analysis of classical software applications. It is well known that program analysis is a prohibitive task. Dynamic program analysis of programs is not reliable since it is intrinsically affected by false negatives, in that issues in programs can be identified during testing only if the testing suite exploits them. Static program analysis is, from this point of view, a much more promising alternative because it has the potential of being sound; in other words, at least theoretically, static program analysis should be able to have no false negatives. The problem with static program analysis is that, since its findings are based on a mathematical model of each program under analysis, for such model to be sound it has to overapproximate the set of all the program states that the program can take at run time. 
Such overapproximation inevitably leads to false positives. Today’s programs are very large and complex, and depend on additional metadata associated with them, such as deployment descriptors and configuration files. In order to build a sound mathematical model of such a large and complex program, a static analyser has to sacrifice precision, which means that the number of false positive quickly becomes very high—so much so that developers refuse to use static analyzers because of the time wasted in filtering out false positives. In order to reduce the number of false positives, numerous solutions have been proposed, but they all boil down to increasing the model’s precision, which in turn reduces the scalability of the analyser. The main goal of the Q4SE project will be to devise new QC algorithms for static analysis of classical programs, taking advantage of the computational power of quantum computers to resolve the well-known scalability/precision dichotomy