Positions: 10

Research Grant (BI)

  • Refª BI|2025/634 – Project CRAI – refª C628696807-00454142

    Type of position: Research Grant (BI)
    Duration: 6 months
    Deadline to apply: 2025-01-07
    Description

    To develop new methods to improve the efficiency of machine learning training, inference, fine tuning, and/or hyperparameter optimization.

    To implement the methods that were developed, evaluate their efficiency, and apply them, possibly in the context of applications such as id verification.


    Contact email: bolsas@inesc-id.pt

  • BI|2025/632-CRAI– Refª C628696807-00454142

    Type of position: Research Grant (BI)
    Duration: 6 months
    Deadline to apply: 2025-01-07
    Description

    LLMs, or large language models, like ChatGPT, do not possess long-term memory in the same way humans do. While they can retain information during a conversation or within a limited context, they do not retain knowledge or information from one session to another. Each time you interact with an LLM, it starts with a blank slate and does not have access to any previous conversations or information. To create virtual and embodied agents that are believable and create meaningful interactions and relationships agents must remember their interacting partners. In this thesis you will explore the creation of long-term memory mechanisms in a robotic agent and study the impact on human-robot interaction. You will design and implementation of a long-term memory system that grounds external knowledge and persona simultaneously to support dialogue generation; This includes exploring the creation of mechanisms that enable encoding and retrieval of events in knowledge structures that make the self (Persona).


    Contact email: bolsas@inesc-id.pt

  • Refª BI|2025/635 EMPOWER – Refª 101060918

    Type of position: Research Grant (BI)
    Duration: 6 months
    Deadline to apply: 2025-01-07
    Description

    EMPOWER project intends to create a novel platform based on new paradigms from psychology that suggest that Executive Function (EF) and Emotional Self Regulation are two key sets of skills to target children with neurodevelopmental disorders (NDDs). The core output of EMPOWER is thus an educational platform, consituted by several computer games, co-created by all involved 

    stakeholders, that facilitates the improvement of these key skills in children with NDDs.  As part of this project the candidate will carry out research activities to collect data, analyse and build models that integrate gaze and HRV with game behaviour and performance to create better learning experiences.


    Contact email: bolsas@inesc-id.pt

  • Refª BI|2025/633-GNS-Chaves

    Type of position: Research Grant (BI)
    Duration: 6 months
    Deadline to apply: 2025-01-03
    Description

    Investigation and development of portable , data-parallel and cross-platform approaches for efficient processing of high-error Low-Density Parity Check (LDPC) codes on GPU devices in the context of Quantum Key Distribution (QKD).


    Contact email: bolsas@inesc-id.pt

  • Refª BI|2025/629-HLT – BM - AC07103

    Type of position: Research Grant (BI)
    Duration: 4 months
    Deadline to apply: 2024-12-27
    Description

    Contact email: bolsas@inesc-id.pt

  • Refª BI|2025/630 SYCLOPS – refª 1010192877

    Type of position: Research Grant (BI)
    Duration: months
    Deadline to apply: 2024-12-27
    Description

    nvestigation and development of portable and cross-platform approaches for efficient processing of highly irregular and multidimensional data, such as sparse tensors and their decompositions, in highly heterogeneous computing systems equipped with state-of-the-art proceeding in/near memory technologies.


    Contact email: bolsas@inesc-id.pt

  • Refª BI|2025/631 - SYCLOPS – refª 1010192877

    Type of position: Research Grant (BI)
    Duration: 6 months
    Deadline to apply: 2024-12-27
    Description

    Investigation and development of innovative insightful models to characterize the performance, power consumption, and energy-efficiency upper-bounds of the GPU and AI-based processing architectures for accuracy and efficiency characterization of different DNNs.


    Contact email: bolsas@inesc-id.pt

Contract