Positions: 6

Research Grant (BII)

  • HG-IDSS - UIDB/50021/2020 (DOI:10.54499/UIDB/50021/2020)-BII|2024/539

    Type of position: Research Grant (BII)
    Duration: 3 months
    Deadline to apply: 2024-06-19
    Description

    Implementation of the first version of a Clinical Data registry for Children with Chronic and Complex Diseases:

    implementation of the relational database; design and implementation of the GUI; implementation of the application logic that connects the GUI with the database; secure authentication and data access segmentation by user.


    Contact email: rh@inesc-id.pt

Research Grant (BI)

  • project Center for Responsible AI – Refª C628696807-00454142 - BI|2024/536

    Type of position: Research Grant (BI)
    Duration: 4 months
    Deadline to apply: 2024-07-22
    Description

    Design, develop and evaluate methods for estimating the expected impact of retraining or fine-tuning machine learning models.  The objective is to automate the decision process underlying the choice of undergoing computational expensive update processes of Machine Learning (ML) models, in order to contribute to the establishment of sustainable AI systems. The grant assignee will look at machine learning models that target a spectrum of application domains, ranging from financial fraud detection to NLP.


    Contact email: rh@inesc-id.pt
  • Project BIG – Refª 952226-BI|2024/544

    Type of position: Research Grant (BI)
    Duration: 12 months
    Deadline to apply: 2024-06-25
    Description

    The Executive Board of INESC-ID – Instituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa, approved a call for one scholarship for a Ph.D. student in the scope of project ERA Chair Project - BIG: “Enhancing the research and innovation potential of Tecnico through Blockchain technologies and design Innovation for social Good”, Grant Agreement nº 952226, funded by the European Union. This project envisions building a digital living lab infrastructure (BIGLab), which will serve as a shared cross-domain platform that will foster interdisciplinary approaches and the exploitation of synergies among research members and collaborators in areas related to blockchain.

     

    The Ph.D. student scholarship position will contribute to fulfilling the project´s goals by:

     

    Providing a funding opportunity to Ph.D. students who are developing research in Blockchain related areas, Providing support to the project´s research team; Conducting research in the areas of blockchain security and robustness, namely by devising systematic ways to understand, compare, and improve the performance of large-scale blockchains in general, and blockchain consensus protocols in particular, in scenarios that require security guarantees against deliberate adversarial behavior from a subset of the participants.


    Contact email: rh@inesc-id.pt
  • RELEVANT–refªPTDC/CCI-COM/5060/2021-BI|2024/540-BI|2024/541

    Type of position: Research Grant (BI)
    Duration: 6 months
    Deadline to apply: 2024-06-20
    Description

    The successful candidates will contribute to the development of evaluation scenarios for reinforcement learning algorithms in non-stationary environments. Specifically,

    • One of the grantees is expected to investigate/develop scenarios of evaluation involving populations of social-learning agents. In particular, the work will involve the development of models of human social intelligence (SI) based on game theory that support of reputation-based cooperation. The grant holder will define and implement agent models and run simulations of populations grounded on evolutionary game theory to study how different dimensions of SI evolved to manage information crucial to maintaining reputation-based cooperation in a noisy world. The development of such agent models will then allow the construction of agent populations with time-varying behavior that can be used as a non-stationary evaluation testbed.

    • Another grantee is expected to investigate/develop game scenarios for evaluation, where the game levels are generated automatically. In particular, the work will involve developing algorithms to generate levels for collaborative games. This work will improve existing solutions by exploring genetic algorithms and deep learning. The work will study different ways of representing this dynamic.


    Contact email: rh@inesc-id.pt
  • RELEVANT – refª PTDC/CCI-COM/5060/2021 BI|2024/542

    Type of position: Research Grant (BI)
    Duration: 6 months
    Deadline to apply: 2024-06-20
    Description

    The grantee will develop new deep-learning approaches to extract relevant features in large datasets to understand the  environment's causal structure. These structures can then be used in complex prediction problems, e.g. environmental data, or in planning problems by combining them with reinforcement learning.


    Contact email: rh@inesc-id.pt
  • Proj. INNO4SCALE- CMB4SCALE– refª 101118139-BI|2024/543

    Type of position: Research Grant (BI)
    Duration: 6 months
    Deadline to apply: 2024-06-20
    Description

    The CBM4Scale innovation study is a cutting-edge research initiative aimed at advancing the performance and scalability of Graph Neural Network (GNN) models on extreme-scale HPC systems. By pioneering a novel Compressed Binary Matrix (CBM) storage format and optimized matrix operations, this project seeks to achieve groundbreaking advancements in AI applications across various domains.

    Within this consortium, you will be at the forefront of developing, evaluating, and integrating innovative algorithms to redefine the capabilities of current GNN frameworks.

    The main objectives of this scholarship is to study and research the fundamentals of GNN architectures, their capabilities for extracting and learning rich features on real-world graphs, and how underlying algorithms can be accelerated by using more efficient matrix operations and/or approximation schemas.


    Contact email: rh@inesc-id.pt