The large hadron collider (LHC) at CERN is being upgraded, to improve its performance. One of the detectors of the ATLAS/LHC experiment is TileCal, the Tile hadronic calorimeter, which is also undergoing an upgrade effort.
A Portuguese team from several Institutions is developing the high voltage (HV) system of the Tilecal for the upgrade, which includes the HVremote and HVsupplies boards that regulate the power delivered to photomultiplier tubes (PMT), and their control systems. The system was to be tested at Geneva's CERN site, but due to the current health & safety constraints in Europe is being tested in Lisbon.
The selected candidate will work on some of the following tasks: 
    • Setup of a test system to evaluate the response of PMTs excited by a light source, when using the new HV system prototypes supplying the PMTs active voltage dividers;
    • Test of the next prototype of the HVremote board;
    • Design/test of aHV cable tester;
  The candidate will be part of the ATLAS Portuguese team and will work with the TileCal upgrade team.

This scholarship will Investigate the generation and impact of creative AI explanation approaches, such as time-based visualisations and infographics, sound/music-based, in order to increase user engagement, understandability, and acceptance of AI explanations in the context of information manipulation. Social and psychological aspects will be taken into account in the aim of producing personalised XAI.

BI| 2021/159 
This project seeks to contribute to the analysis and automatic detection of online hate speech in Portuguese, investigating the linguistic and rhetorical strategies underlying both overt and covert hatred content. Specifically, this project involves the creation of a large annotated corpus from social media, covering the Covid-19 pandemic, which will support the development of a machine learning prototype to detect hate speech, and assess its explicitness and intensity, considering the time period and geolocation data.
INESC-ID aims to recruit one BI-Licenciado to work on the development of semiautomatic strategies for data collection and annotation, and on the creation of a prototype that demonstrates how the information in the annotated corpus can support hate speech detection.
BI| 2021/160 
This project seeks to contribute to the analysis and automatic detection of online hate speech in Portuguese, investigating the linguistic and rhetorical strategies underlying both overt and covert hatred content. Specifically, this project involves the creation of a large annotated corpus from social media, covering the Covid-19 pandemic, which will support the development of a machine learning prototype to detect hate speech, and assess its explicitness and intensity, considering the time period and geolocation data.
INESC-ID aims to recruit one BSc to work on the development of a prototype that demonstrates how the information in the annotated corpus can support hate speech detection.

IntelligentCare project develops a multimorbidity management decision support system focused on the patient's vision of value, i.e., achiev-ing 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 technolo-gies. 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. 

Serverless is an increasingly popular cloud programming model that offers automatic scalability with high elasticity, per-request billing, and zero infrastructure management. 
Serverless functions, however, run inside containerized runtimes such as Java Virtual Machines, JavaScript Engines, Python Interpreters, requiring a separate container (or VM) and runtime for each invocation. This leads to high resource duplication and high latencies.
This project aims at taking advantage of GraalVM, a next-generation JVM capable of running many languages and compiling code ahead-of-time, to solve many of the shortcomings of current Serverless platforms.

Object Oriented Programming has flourished in many areas ranging from web-oriented microservices, data processing, to databases. However, while representing domain entities as objects is appealing to developers, it leads to high data fragmentation as data is loaded into applications as large collections of data objects, resulting in high memory footprint and poor locality.
To minimize the memory footprint and improve locality, this project will look into how to optimize the layout of objects inside widely used data structures such as Maps and Lists. The goal is to take advantage of GraalVM Native Image to statically analyze the application code and verify that some data layout transformations are possible.

Pattern recognition and statistical analysis of timestamped event data, including the proposal of null models and statistical tests to understand meaningful deviations. 
Development of a software application for the recognition of anomalies from timestamped event data. 
The grant holder will develop contributions with potential translation and applicability to the challenges within Task 4 of the ILU project.

The main goal of this work will consist in the design and development of the solutions to address the project’s challenges of text visualization, navigation and annotation.

Tech4SocialGood aims to develop, pilot test, and study the efficacity of a teacher training program to promote both engagement as well as competence on the subject of cyberbullying. The grant holder will work on the technological components of a multi-modal learning platform (a serious game) that will be developed to that end. This will entail both research and the implementation of state-of-the art techniques from interactive digital storytelling, intelligent virtual agents and affective computing, that will support the creation of the serious game.

The selected candidate will be a member of the research project PassCert, which aims at building an open-source, proof-of-concept password manager that through the use of formal verification, is guaranteed to satisfy properties on data storage and password generation. PassCert is a research collaboration between researchers from INESC-ID Lisboa (PT), INESC TEC (PT), and Carnegie Mellon University (US).
The selected candidate will:
 1) Contribute to the development of the proof-of-concept verified password manager.
 2) Explore ways in which the data security of the password manager can improve.
 3) Investigate ways of formally verifying the data security properties identified.
 4) Actively participate in and contribute to PassCert's activities and regular meetings.

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 Model and Dialog System components.

The candidate will work on Task 5 of the Cosmos project, namely on “Implementation and Evaluation”. The concrete task of the candidate will implement and evaluate algorithms to offer transactional guarantees for edge storage systems.