The goal of this project is to implement a synthesizer for code search queries from examples. This synthesizer should receive an OutSystems code base and an initial code snippet in that codebase as an example, synthesize a broad query that captures that snippet, and further refine it by leveraging feedback from the user regarding which reported snippets are desired and which ones are not.

- Develop tools allowing the optimal management of V2X considering the integration in different type of buildings. - Test electric vehicles management strategies.

- Develop tools allowing the optimal management of V2X considering the integration in different type of buildings. - Test electric vehicles management strategies.

- Develop a simulation platform based in multi-agent´s systems allowing the simulation of the impact of mass development of electric vehicles in smart cities. - Test electric vehicles management strategies.

: Investigation and exploration of novel methods for high-order epistasis detection based on machine and/or deep learning, with a special focus on exploiting sparsity features in the state-of-the-art neural networks, as well as on the evaluation of efficiency on the emerging AI-oriented computing devices.

Investigation and exploration of novel methods for efficient processing of machine learning applications, with a special focus on heterogenous systems, sparse computations and modern programming frameworks.

Outbreaks for Disease surveillance in a data science context) explores the extraction, analysis, and visualization of health-related data (including also environmental data) to enhance the utility of event-based epidemiological surveillance. INESC-ID is hiring one researcher to work on work-package 3 within the project, specifically targeting tasks related to natural language processing for the extraction of information related to disease outbreaks from online media news. Specific tasks to be researched include (a) document classification, (b) key-phrase extraction, and (c) text geo-coding/geo-parsing.

INESC ID is opening a call for the Attribution of 6 (six) of Research Grants, hereinafter referred to as Research Grants for Doctoral Degrees in the scientific areas listed below, under the FCT Research Grant Regulation (RBI) and the Statute of the Scholarship Holder of Research (EBI). THE IMPACT OF THE RESEARCH DEVELOPED AT INESC-ID FOCUSES ON FOUR THEMATIC LINES: Digital Transformation Citizenship Health and Life Technologies Energy transition Security and Privacy THE THEMATIC LINES OF INESC-ID PROMOTE SYNERGIES BETWEEN THE FOLLOWING 11 SCIENTIFIC AREAS: Artificial Intelligence for People & Society Automated Reasoning & Software Reliability Communication Networks Distributed Parallel and Secure Systems Graphics and Interaction Green Energy & Smart converters High Performance Computing Architectures and Systems Human Language Technologies Information & Decision Support Systems Nano-Electronic Circuits & Systems Sustainable Power Systems. The grants are funded by the Foundation for Science and Technology (FCT) under the Collaboration Protocol for Financing the Multi-Annual Plan for Research Grants for Doctoral Students, signed between FCT and INESC-ID, Institute of Systems and Engineering Computers: Research and Development in Lisbon, with the reference UIDB/50021/2020.

This scholarship will Investigate the generation and impact of creative AI explanation approaches, such as text-based, image-based, 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.

CTTRC#1 Normalization of geolocation records using deep learning techniques Task: Apply deep learning techniques to the problem of normalizing records in databases, namely addresses and other geolocation references. The system to be developed must infer, from previous records, the correct mapping between the input records and the records of a standardized database.

CTTRC#2 High confidence information retrieval and question answering Task: In the context of the Center for Responsible AI project, the aim is to develop state-of-the-art techniques for information retrieval and question answering, based on deep learning models, that generate high-quality, explainable, answers to complex queries.

CTTRC#3 Application of reinforcement learning methods to the generation of efficient algorithms Task: Deep reinforcement learning methods have been extensively applied in sequential decision optimization, and have advanced the state-of-the-art in areas like game playing, robotics and planning. The selected candidate will apply deep reinforcement learning methods to the optimization of methods and algorithms in different areas of application related to the Responsible AI project.