Recently, seven INESC-ID projects were approved by the Foundation for Science and Technology (FCT) in the Call for SR&TD Projects in all Scientific Domains 2023. The call will support internationally recognised scientific research and technological development (SR&TD) projects, in all scientific domains, aiming to contribute to innovative processes, with market purpose, and increase knowledge creation to respond to business and societal challenges.

It was launched in conjunction with Innovation and Digital Transition Programme – COMPETE 2030 and the Regional Programmes of the North, Centre, Lisbon, Alentejo and Algarve, and the projects may be promoted in the form of individual projects or in co-promotion, with a maximum duration of 36 months and with a maximum eligible investment of € 250,000.

INESC-ID had the following projects approved:

Serverless High-density Environment for eLastic cLouds
PI: Rodrigo Bruno
Cloud computing is currently in an impasse. While hardware efficiency is improving at an exponentially lower rate, the demand for elastic and scalable cloud resources keeps growing, as evidenced by the emergence of popular cloud computing models such as Serverless. This demand cannot be met with existing virtualization technology, and to tackle this challenge, the project will propose Shell, a new virtualized runtime environment that has the potential to unlock the new cloud era.

VERSACOMP: VERSAtile COMPuting with Advanced Processor Architectures
PI: Ricardo Nobre
The advent of processors with Artificial Intelligence (AI) capabilities represents a significant leap forward. This project aims at redefining the boundaries of what is possible with general-purpose computing. Unlocking the full capabilities of modern heterogeneous systems with AI-enhanced processors for a broader range of computational tasks can result in substantial improvements in performance, energy efficiency, and cost-effectiveness.

GLOG: A shared log for building distributed applications
PI: Luís Rodrigues
This project aims at designing and implementing a highly efficient distributed shared log, a data structure that allows the view of events occurring in a distributed system, named Global LOG (GLOG). It will combine features of classical logs with features of publish-subscribe systems, in a unique combination of flexibility and global consistency, able to support for geo-distributed operation, subscribers with different consistency requierements and clients concerned with a subset of the information in the log.

SYNTHESIS: Mosaic interaction and synthetic generation of multi-omics data for the discovery of precision medicines for cancer
PI: Emanuel Gonçalves
Despite remarkable progress in cancer precision medicine, drug resistance and low success rates underscore the urgent need for data-driven clinical trial designs. SYNTHESIS uses generative deep learning to integrate multi-omic data from pre- and clinical cancer databases, generating synthetic tumor profiles. This will aid the identification of druggable breast cancer subtypes and their molecular signatures, ultimately boosting clinical trials.

SafeIaC: Reliable Analysis and Automated Repair for Infrastructure as Code
PI: João Ferreira
Software systems are foundational to the functioning of critical infrastructure, but errors in specific pieces of their code are a significant source of system failures and degradation, compromising the stability and reliability of essential services. In collaboration with INESC-TEC, SafelaC aims to develop methodologies for the reliable analysis and automated repair of software configuration and infrastructure code, contributing to a more resilient digital infrastructure. In the end, it has the potential of benefiting industries and end-users alike by enhancing the stability and security of critical services.

SWATE: Socially-Aware AI for Teamwork Enhancement and Training
PI: Joana Campos
Assessing embodied interactions in physical environments over time can provide valuable insights of team dynamics. SWATE explores the creation of a Socially-Aware AI agent to enhance team training by providing real-time insights and actionable feedback from 3D articulated bodies and audio signals, as descriptors of teamwork dynamics. SWATE promises to revolutionize team dynamics understanding and applications across various domains.

Configurable Neural Processing Unit for Embedded Intelligence
PI: Mário Véstias
The goal of the project is to prototype a configurable neural processing unit for embedded AI inference and training. The expected results include a configurable Neural Processing Unit and a hardware-oriented model design framework for embedded AI. The novel Neural Processing Unit will contribute to embedded AI expansion by improving computing and energy efficiency and allowing the adaptation of the architecture to the heterogeneity of the models.

More information about the Call (in Portuguese) here