Positions: 10

Research Grant (BI)

  • BI|2026/872-Project ORQESTRA

    Type of position: Research Grant (BI)
    Duration: 6 months
    Deadline to apply: 2026-03-27
    Description

    ONE (1) research grants for students with MSc degree with reference number BI|2026/872, project ORQESTRA - Refª 101224573, funded by European Commission - Program HORIZON EUROPE, is now available under the following conditions:

    OBJECTIVES | FUNCTIONS 

    Develop high efficient and secure systems based on the neuromorphic computing paradigm. The work to be developed consider security supported on    spike-based computing, where computation is triggered by input spikes. The efficiency inherent to this type of processing will be explored also on this new important perspective. 


    Contact email: bolsas@inesc-id.pt

  • BI|2026/869-Projet DIME-refª 2024.13433.CMU

    Type of position: Research Grant (BI)
    Duration: 5 months
    Deadline to apply: 2026-03-24
    Description

    ONE (1) research grant for students with BSc degree with reference number BI|2026/869 under the scope of the Projet DIME-FS A Decentralized File System  – refª  2024.13433.CMU,  funded by Fundação para a Ciência e a Tecnologia, is available under the following conditions:

    OBJECTIVES | FUNCTIONS 

    This project aims to design and implement DRIFT (Decentralized Reputation Infrastructure for Trust), a novel token-based reputation system designed for decentralized cloud computing platforms governed by Decentralized Autonomous Organizations (DAOs). It tackles the fundamental challenge of establishing trust in peer-to-peer computing resource markets without relying on centralized authorities. DRIFT leverages a unique reputation token that serves both as a governance tool and a marker of reliability, creating strong economic incentives for providers to deliver consistent, high-quality services. The system integrates attestation protocols and a stake-weighted reputation model that dynamically reflects verified contributions of computing resources. 

     

    In contrast to traditional centralized cloud reputation frameworks—often opaque and controlled by corporate entities—DRIFT offers a transparent, community-driven alternative. Unlike decentralized platforms such as Filecoin and Golem, which employ rudimentary reputation features, DRIFT’s dual-purpose token introduces a significant innovation by merging governance power with economic rewards. Its stake-weighted scoring mechanism addresses the cold-start problem, enabling new participants to signal credibility through initial token commitments. Moreover, DRIFT’s attestation protocols provide cryptographic guarantees of service quality, surpassing the basic completion metrics used by other systems. This multifaceted approach blends economic incentives with verifiable trust signals, offering a more nuanced and robust reputation framework for decentralized cloud infrastructures.


    Contact email: bolsas@inesc-id.pt

  • BI|2026/870-Project ACCELERAT.AI

    Type of position: Research Grant (BI)
    Duration: 3 months
    Deadline to apply: 2026-03-24
    Description

    ONE (1) research grant for students with BSc degree with reference number BI|2026/870 under the scope of the Project ACCELERAT.AI – Refª C644865762-00000008 funded by by Recovery and Resilience Plan (RRP) https://recuperarportugal.gov.pt/ and Next Generation EU European Funds, is available under the following conditions:

    OBJECTIVES | FUNCTIONS 

    The remarkable text generation capabilities shown by LLMs have sparked the interest of researchers working in automatic speech recognition to explore approaches to incorporate the speech modality, so that the model can be fine-tuned to perform automatic speech recognition. There are several methods to do this “connection”, including the use of discretised new units and the adaptation of the speech encoder embedding space to that of the language model.

    This project proposal will focus in the former. Similarly to previous work [1], we plan to incorporate speech modality via discretized units to a European Portuguese LLM. Some aspects that will require particular attention is the evaluation and selection of the speech encoder for Portuguese, the configuration of the discretization process and the fine-tuning with (likely) less training resources than previous experiments with high-resourced languages, such as English.


    Contact email: bolsas@inesc-id.pt

  • BI|2026/865-Projet DIME-FS

    Type of position: Research Grant (BI)
    Duration: 5 months
    Deadline to apply: 2026-03-24
    Description

    ONE (1) research grant for students with BSc degree with reference number BI|2026/865 under the scope of the Projet DIME-FS A Decentralized File System  – refª  2024.13433.CMU,  funded by Fundação para a Ciência e a Tecnologia, is available under the following conditions:

    OBJECTIVES | FUNCTIONS 

    The goal of this project is to design and implement an overlay network architecture for DIME-FS, a decentralized file system designed to for high performance. The project will focus on adaptive topology management algorithms that dynamically organize nodes based on geographic proximity, network latency, storage capacity, and trust metrics derived from historical behavior. The data distribution heuristics implement a hybrid replication strategy that balances availability requirements with performance considerations, utilizing a risk-scoring mechanism to identify potentially malicious peers while minimizing unnecessary redundancy. The system uses a reputation-based access control mechanism where data placement decisions incorporate peer trustworthiness scores derived from participation history, network contributions, and validation consensus.


    Contact email: bolsas@inesc-id.pt

  • BI|2026/863-Project YODA-LISBOA2030-FEDER-00882900

    Type of position: Research Grant (BI)
    Duration: 6 months
    Deadline to apply: 2026-03-17
    Description

    ONE (1) research grant for students with BSc degree with reference number BI|2026/863 under the scope of the Project YODA - Your Own Developmental Agent: Agent-led feedback for improving team interpersonal processes – Refª LISBOA2030-FEDER-00882900, funded by operation nº 15166, Balcão dos Fundos, FEDER and FCT, is now available under the following conditions:

    OBJECTIVES | FUNCTIONS 

    The main objective of this scholarship is to support the development and validation of the YODA artificial intelligence agent by contributing to two core components of the project: (i) the training of the agent for the detection of affective and conflict-related interaction patterns, and (ii) the qualitative analysis of team interaction data. The scholarship holder will work at the interface between machine learning outputs and organizational behavior research, ensuring that automatically detected patterns are meaningfully interpreted in terms of interpersonal team processes.

    Specifically, the scholarship aims to:

    - Support the training and fine-tuning of the AI agent using multimodal data (primarily text and, when applicable, audio/video) to identify affective states and conflict-related indicators in team interactions;

    - Contribute to the qualitative identification, interpretation, and labeling of recurrent interaction patterns reflecting key interpersonal processes in teams;

    - Assist in translating qualitative insights into prototypical interaction patterns that can be used as triggers for agent-led feedback.

     

    Work Plan of the Scholarship Holder

    The scholarship will be structured into two interconnected project tasks:

    Task 4 – Training of the Agent

    - Familiarization with the project’s conceptual framework, including theories of team interpersonal processes and sentiment analysis approaches adopted in the project;

    - Preparation, organization, and preprocessing of datasets to be used in the training of the AI agent;

    - Support in the training and fine-tuning of machine learning models for detecting affective tone, conflict indicators, and interactional cues in team communication;

    - Preliminary validation of the agent’s outputs in close collaboration with the machine learning and organizational behavior researchers.

    Task 5 –Qualitative Analysis

    - Participation in the qualitative analysis of team interaction data (e.g., video recordings, transcripts, and chat logs) collected in laboratory studies;

    - Coding of interaction sequences to identify recurrent patterns related to affect regulation, conflict emergence, and interpersonal dynamics;

    - Integration of qualitative findings with the patterns identified by the AI agent, contributing to the definition of prototypical interaction patterns;

    - Systematic documentation of analytical procedures and results to support subsequent feedback development and empirical testing phases.

    Throughout the scholarship period, the scholarship holder will work under the supervision of senior researchers from both machine learning and organizational behavior, participate in regular project meetings, and contribute to internal technical reports and, where appropriate, to scientific outputs of the project.


    Contact email: bolsas@inesc-id.pt

  • BI|2026/866 - Projet CCLoud

    Type of position: Research Grant (BI)
    Duration: 3 months
    Deadline to apply: 2026-03-17
    Description

    ONE (1) research grant for students with BSc degree with reference number BI|2026/866 under the scope of the Project CCLoud with the refª 2023.16986.ICDT, funded by Fundação para a Ciência e a Tecnologia I.P., is now available under the following conditions:

    OBJECTIVES | FUNCTIONS 

    To conduct an in-depth study of interplay between carbon efficiency and performance in serverless Function as a Service (FaaS) platforms.

    Investigate how low-latency and high-throughput function designs affect total carbon footprint. Assess how incremental performance improvements impact the energy spent per invocation and total system power, and the embedded carbon intensity associated with the hardware utilized will be factored in to determine both energy and carbon-efficiency. Determine efficiency curves and identify thresholds beyond which performance tuning yields diminishing returns relative to carbon cost. Integrate work outcomes in an understandable model that users (FaaS developers) and providers can employ to balance latency and carbon footprint.


    Contact email: bolsas@inesc-id.pt

  • BI|2026/868 - Projet CCLoud – refª 2023.16986.ICDT

    Type of position: Research Grant (BI)
    Duration: 3 months
    Deadline to apply: 2026-03-17
    Description

    ONE (1) research grant for students with BSc degree with reference number BI|2026/868 under the scope of the Project CCLoud with the refª 2023.16986.ICDT, funded by Fundação para a Ciência e a Tecnologia I.P., is now available under the following conditions:

    OBJECTIVES | FUNCTIONS 

    To conduct an in-depth study of graph processing considering energy efficient and carbon intensity in edge-cloud and cloud-continuum scenarios.

    Investigate the implications of current movements to push the cloud towards the edges of the networks, where big, remote, energy-hungry datacenters are combined with more widespread edge infrastructures where data can be processed with reduced latency and increased privacy.

    Assess how a decentralized graph storage and processing system can be made aware of computational limitations. Combine the graph processing engine with peer-to-peer storage for decentralization. Evaluate the implications in latency and throughput when processing is performed at the same location as the data, when possible, and how this can contribute to increased energy efficiency and reduced carbon intensity.     


    Contact email: bolsas@inesc-id.pt

  • BI|2026/867-Projet CCLoud – refª 2023.16986.ICDT

    Type of position: Research Grant (BI)
    Duration: 3 months
    Deadline to apply: 2026-03-17
    Description

    ONE (1) research grant for students with BSc degree with reference number BI|2026/867 under the scope of the Project CCLoud with the refª 2023.16986.ICDT, funded by Fundação para a Ciência e a Tecnologia I.P., is now available under the following conditions:

    OBJECTIVES | FUNCTIONS 

    To conduct an in-depth study of interplay between carbon efficiency and performance in workflow/dataflow execution in cloud platforms.

    Investigate how workflow-based applications are widely used in cloud and cluster environments to coordinate complex computations, including AI workloads, with increasing energy and carbon footprints. Assess how carbon-aware scheduling techniques such as shifting workloads in time, across space, migrating them across regions, or adjusting allocated resources can significantly reduce emissions. Combine the application of such techniques, often employed only isolated. Develop a carbon-aware workflow execution framework capable of integrating real-time carbon intensity information into workflow scheduling decisions.


    Contact email: bolsas@inesc-id.pt

Contract