1)       The definition of the architecture of a user-centric network virtualization platform.
2)       The design, implementation, and evaluation of a compiler that allows users of the platform to program their network elements using a high-level language (specifically, P4) to customize non-conventional packet processing (specifically, an ICN forwarding module).
The work plan is included in task 3 (Virtual network embedding, orchestration, and migration) and task (Integration, analysis and evaluation of the use cases) do projeto uPVN, User Centric Programmable Virtual Networks,

2 research grants with reference number BI|2020/85 and BI|2020/86 are now available under the scope of project GOLEM (45917) CALL 04/SI/2019, funded by FEDER, PORLISBOA, ANI and FCT . 
BI|2020/85 – Activity a) The main goal is to extend the tool FOREST, an interactive multi-tree synthesizer for regular expressions.
BI|2020/86 – Activity b) The main goal is to extend the tool CUBES, a parallel program synthesis tool, to exhibit a deterministic behaviour.

1) Obtain a benchmark offering real vulnerabilities test cases to study the approaches developed in the context of the project (task T1). 
2) Study the performance of static analysis tools using the benchmark obtained in task T1 (task T2). This is to understand which of them are more efficient and which of them may be complementary. Through this process, this task will label several warnings as false positives or not. Furthermore, based on the labels, develop a method to prioritise warnings. There is already some research in alert prioritization using labeled warnings has input. After collecting the data and studying the tools, the team plans to explore solutions to several different problems of static analysis. 
3) Explore the combination of a set of tools studied in task T2 (task T3). The goal is to explore and design a unified technique that intelligently will combine the different techniques to report more warnings. Following CodeAware’s vision, the researcher will also explore how to improve software engineers’ CI experience dealing with high numbers of warnings, understanding the problems and customizing their own analysis in terms of tooling, types of vulnerabilities and files. We will also explore novel approaches to rank warnings. This will provide more efficient rankings of warnings that can pave the way to other research fields such as Automated Program Repair field. Following CodeAware’s vision, the team plans to explore how to design customized, intelligent notifications system that will take into consideration the warnings rankings and the security engineers experience, roles and availability. Exploring visualizations and ways of dealing with the different types of warnings will also be studied in this research project.

ICare4U is a project that proposes the development of a decision support system based on intelligent modelling and patient sub-group analysis, to provide personalized therapy for critically ill patients.
It is hypothesized that the 'Collective Experience' from large clinical databases, where clinical decisions are linked with patient outcomes, can be used to identify specific patient sub-groups and build personalized therapy models towards a new era of personalized medicine, allowing the improvement of patient outcomes in the ICU. The validity of the proposed systems will be tested using two case studies where generalized severity scoring systems have consistently performed poorly: patients admitted to the ICU who then develop acute kidney injury, where two-thirds of these patients require renal support therapy; and severe sepsis a typical heterogeneous disease among critically ill patients, in which the immune response is highly dynamic
and variable, and likely to need complex therapies to improve patient outcomes.
One of the tasks of the project involves using existing text-based information, such as medical reports and physicians and nurses’ notes, to improve the decision-making process. The work to be developed by the researcher will be in the context of this task, and consists of implementing and integrating a technique called “Fuzzy Fingerprints” in the decision support system, and to enhance and integrate Fuzzy Fingerprints with Deep Learning.
Digital fingerprints aim to extract information about a user that he/she has not provided knowingly, and represent such information in a compact and efficient form. Fuzzy fingerprints based on the order of top-k events have been successfully applied in many areas, including text mining, and will be used in this project to help identifying critically ill patients (by creating patient sub-group fingerprints). The technique presents a simple but extremely effective and robust way to evaluate and compare the relevant features of event patterns, which in the present case would be the text contained in the physicians/nurses notes. Important phrases and/or keywords will be used as events and event patterns. A text based fingerprint will be created for each admitted patient. A sliding window top-k algorithm will be adapted to create an evolving patient fingerprint algorithm to cope with patients’ evolving condition. Patients will be attributed to patient sub-groups based on fuzzy fingerprint similarity.
The MIMIC II database will be used for this project. All text data will have to be “cleaned up” on a pre-processing phase since textual medical databases have several particularities that prevent its immediate and effective use.

In this grant, the student will perform conversational quality estimation, both in local and global levels. That is, he/she will evaluate the success of each suggested response in terms of its intent, semantics, register, etc., and we will also perform an overall analysis of agent/user satisfaction. For this, we will take advantage of the feedback provided by human agents (The provided answers were selected or not? In which position did the human agent select the answer? Did the human agent edit an answer?), and also of the outcomes regarding the sentiment analysis studies. This information should be added to the learning process of the digital assistant, so that it improves its answering capabilities over time. 

Develop artificial personas for automated UX testing of interactive systems. These personas should define different motivations and different strategies for the interaction with an environment under test. The personas will be implemented as AI agents combining ML and search and may include a cognitive affective model to drive the decisions.

3 research grants with reference number BI|2020/82, BI|2020/83 and BI|2020/84 are now available under the scope of project GOLEM (45917) CALL 04/SI/2019, funded by FEDER, PORLISBOA, ANI and FCT.
BI|2020/82 - activity a) The main goal is to develop pure function synthesis by introducing support for a wide range of constructs, such as database accesses and calls to other pure functions.
BI|2020/83 – activity b) The main goal is to develop an SQL synthesizer for synthesizing SQL queries, from input-output examples, that satisfy a given template.
BI|2020/84 – activity c) The main goal is to develop a new tool to synthesize SQL considering a natural language specification provided by the user.

The candidates should have an MSc in Computer Science, Electrical and Computer Engineering, or equivalent scientific areas. Excellent academic and practical background in programming, machine learning, and natural language processing. The candidate should have good knowledge of written and spoken English, be self-motivated and show initiative in solving problems. Strong preference is given to candidates with sound knowledge of Python and Pytorch.
Required language skills: proficiency in English.

International selection call for a doctorate position under the scope of project UIDP/50021/2020 funded by Fundação para a Ciência e a Tecnologia , in the form of an unspecified fixed-term work contract – Decree-Law No. 57/2016, of August 29, the 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, “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 

The grant holder will be integrated in the INESC-ID team of the BioVisualSpeech project. The project seeks to develop tools for speech therapy of children that can be used in gaming therapy environments together with other modality analysis tools such as video or gaze. 
The main objective of this grant is to contribute to the development of automatic children speech analysis modules under the supervision of Prof. Alberto Abad. Some specific work objectives for this grant position consist of the following tasks, among others:
•	Investigate and develop methods for automatic miss-pronunciation detection and pronunciation evaluation of Children speech productions.
•	Contribute to the integration of the developed modules in the project demonstrators.
•	Participation in the production of relevant documentation.

Management, dissemination and communication of science and research within the scope of project Olissipo. The Doctoral Fellow will be responsible for supporting the Principal Investigator for the overall management of the project, organizing work meetings, helping in the preparation of progress reports, disseminating results obtained in the project and communicating with the community and society in general. The grantee will also have the opportunity to contact partners and international research groups as professional support for the above functions.