Positions: 9

Research Grant (BI)

  • BI|2026/880 Projet SALVE – refª 2024.14936.PEX

    Type of position: Research Grant (BI)
    Duration: 5 months
    Deadline to apply: 4216-05-30
    Description

    ONE (1) research grant for students with BSc degree with reference number BI|2026/880 under the scope of the Projet SALVE: Securing Artificial Language Models Against Vulnerability Encoding (2024.14936.PEX),  funded by Fundação para a Ciência e a Tecnologia, is available under the following conditions:

    OBJECTIVES | FUNCTIONS 

    This task evaluates the impact of controlled code perturbations on the classification stability and robustness of Large Language Models (LLMs) in distinguishing secure from insecure JavaScript code. The student will design and implement a systematic evaluation pipeline to assess model behavior under perturbation-induced variations. The work plan includes:

    Evaluation Pipeline Development (Month 1) Implement a scalable evaluation framework using local LLM infrastructure (e.g., Ollama, LMStudio). Integrate multiple LLMs for comparative evaluation. Automate classification experiments across original and perturbed datasets. Classification Shift Analysis (Month 2) Measure classification changes between original and perturbed code variants. Identify perturbations that cause label flips (secure ↔ insecure).

    Quantify: Misclassification rate, Stability rate, False positive rate, False negative rate

     

    Robustness Assessment (Month 3-4) Define robustness metrics for security classification consistency. Evaluate resilience to obfuscation, control-flow changes, and API variations.

    Compare robustness performance across different models.

     

    Misclassification Characterization (Month 4-5) Construct an augmented misclassification dataset containing: original and perturbed variants, model predictions, correct labels, perturbation type

    Analyze patterns in failure cases.

     

    Exploratory Explainability Analysis (Optional) Investigate whether explainability tools can help identify model reliance on superficial features. Analyze whether models rely on syntax-level heuristics versus security-relevant semantics.

    All experimental artifacts, code, and results will be released in an open-source repository. The selected candidate will be integrated into a research team with established expertise in software security, program analysis, and AI-driven code intelligence, with a track record of collaboration with leading technology companies and publications in top-tier international conferences and journals.


    Contact email: bolsas@inesc-id.pt

  • BI|2026/882 Projet SALVE – refª 2024.14936.PEX

    Type of position: Research Grant (BI)
    Duration: 6 months
    Deadline to apply: 2026-12-31
    Description

    ONE (1) research grant for students with BSc degree with reference number BI|2026/882 under the scope of the Projet SALVE: Securing Artificial Language Models Against Vulnerability Encoding (2024.14936.PEX),  funded by Fundação para a Ciência e a Tecnologia, is available under the following conditions:

    OBJECTIVES | FUNCTIONS 

    This task aims to develop an automated and scalable framework for the continuous improvement of security-aware Large Language Models (LLMs), integrating dataset expansion, evaluation, incremental fine-tuning, and security-aware code generation validation. The student will build an integrated pipeline that reuses artifacts developed in previous tasks and ensures systematic model improvement over time. The work plan includes:

    Automated Dataset Expansion (Month 1) Implement mechanisms to collect and track secure and insecure JavaScript code from open-source repositories. Identify and label security-related commits using diff-based analysis. Integrate synthetic data generation (e.g., AST-based vulnerability injection) to increase dataset diversity. Continuous Model Evaluation (Month 2) Implement automated evaluation of security classification performance on expanded datasets. Measure classification accuracy, precision, recall, and robustness over time. Track performance differentials across evaluation cycles. Incremental Fine-Tuning and Feedback Integration (Month 3) Implement periodic fine-tuning of selected models using curated secure–insecure code pairs. Integrate adaptive feedback mechanisms based on misclassification analysis. Ensure reproducibility and version control of model updates. Security-Aware Code Generation Testing (Month 4) Integrate static analysis tools (e.g., Semgrep, CodeQL) to assess generated code. Measure vulnerability density (e.g., vulnerabilities per 100 lines of code). Compare improvements across pipeline iterations. Validation and Framework Assessment (Month 5-6) Conduct two full validation cycles in the final four months. Measure improvements in: Security classification accuracy Robustness to adversarial modifications Reduction of AI-generated vulnerabilities

    All artifacts will be released as open-source and documented for reproducibility. The selected candidate will be integrated into a research team with established expertise in software security, program analysis, and AI-driven code intelligence, with a track record of collaboration with leading technology companies and publications in top-tier international conferences and journals.


    Contact email: bolsas@inesc-id.pt

  • BI|2026/956 Project SHIFT2DC - Refª101136131

    Type of position: Research Grant (BI)
    Duration: 6 months
    Deadline to apply: 2026-10-09
    Description

    ONE (1) research grant for students with BSc degree with reference number BI|2026/956 under the scope of the Project SHIFT2DC -  Refª101136131 funded by European Commission - Program HORIZON EUROPE, is now available under the following conditions:

    OBJECTIVES | FUNCTIONS 

    Development of a Laboratory Management System (LMS) for the EDL laboratory. Develop a suitable dynamic data model to accommodate different laboratory equipment Development of the LMS software architecture to support automated runs of experiments in the lab Early development of a user interface that enables test definitions.


    Contact email: bolsas@inesc-id.pt

  • BI|2026/955 - Project SHIFT2DC

    Type of position: Research Grant (BI)
    Duration: 6 months
    Deadline to apply: 2026-10-09
    Description

     

    ONE (1) research grant for students with MSc degree with reference number BI|2026/955 under the scope of the Project SHIFT2DC -  Refª101136131 funded by European Commission - Program HORIZON EUROPE, is now available under the following conditions:

    OBJECTIVES | FUNCTIONS 

    Development of control functions for the management of electric vehicles Development of Optimization Models to Parking Lots operation Preparation of simulations in the Laboratory Support the preparation of papers and reports


    Contact email: bolsas@inesc-id.pt

  • BI|2026/957-Proj. SWATE

    Type of position: Research Grant (BI)
    Duration: 6 months
    Deadline to apply: 2026-10-09
    Description

    The Project SWATE was co-funded by the European Union through the Lisboa 2030 Programme (ERDF) and by national funds through FCT, I.P., under project no. 16594. 

    ONE (1) Research Grant for students with BSc degree with the reference number BI|2026/957 under the scope of the Project SWATE: Socially-Aware AI for Teamwork Enhancement and Training– Refª LISBOA2030-FEDER-00777100, is now available under the following conditions:

     

    OBJECTIVES | FUNCTIONS 

    SWATE explores the creation of a Socially-Aware AI agent to enhance team training by providing real-time insights and actionable feedback from multimodal data. We envision that humans and agents will work in teams, complementing each other, due to their different competencies. Teams with high task interdependence spatially organize themselves to facilitate communication, task execution, and action coordination. These spatial arrangements provide valuable insights into team dynamics and coordination processes. The work developed will build upon the segmented motion data captured in the context of  an Escape Room, this subtask will develop machine learning methods to identify and model temporal patterns of team spatial organization and interaction.

    The research will investigate both probabilistic sequence models and generative machine learning approaches. In particular, generative models, such as Generative Adversarial Networks (GANs) will be explored to learn the underlying distribution of team spatial configurations and interaction patterns, enabling the generation and prediction of plausible event sequences and the identification of recurring coordination patterns. Where appropriate, graph-based representations will be incorporated to explicitly model spatial and interaction relationships between team members.

    The resulting models will be used to construct interpretable event storylines that capture how team spatial configurations evolve over time, providing a data-driven characterization of communication, coordination, and task execution dynamics. 


    Contact email: bolsas@inesc-id.pt

  • BI|2026/953 - BI|2026/954 - Projecto AHEAD - Refª 101160665

    Type of position: Research Grant (BI)
    Duration: 6 months
    Deadline to apply: 2026-10-02
    Description

    TWO (2) research grants for students with MSc degree with reference number BI|2026/953 - BI|2026/954, under the project AHEAD -  Refª 101160665, funded by European Commission - Program HORIZON EUROPE, is now available under the following conditions:

    OBJECTIVES | FUNCTIONS 

    Develop data-processing pipelines and ML models for energy applications, considering load or renewable generation forecasting, anomaly and fault detection, predictive maintenance, asset-condition assessment, or identification of critical grid operating conditions.

    Design a blockchain based framework for secure and traceable exchange of data and information.

    Develop an integrated architecture in which ML-generated predictions, classifications or recommendations interact with the blockchain layer

    Assist the research team in producing reports, scientific presentations/publications.


    Contact email: bolsas@inesc-id.pt

  • BI|2026/881 Projet SALVE – refª 2024.14936.PEX

    Type of position: Research Grant (BI)
    Duration: 6 months
    Deadline to apply: 2026-09-30
    Description

    ONE (1) research grant for students with MSc degree with reference number BI|2026/881 under the scope of the Projet SALVE: Securing Artificial Language Models Against Vulnerability Encoding (2024.14936.PEX),  funded by Fundação para a Ciência e a Tecnologia, is available under the following conditions:

    OBJECTIVES | FUNCTIONS 

    This task aims to enhance the ability of Large Language Models (LLMs) to distinguish secure from insecure JavaScript code using contrastive learning with a tailored security-aware loss function. The student will fine-tune selected models using secure-insecure code pairs derived from Tasks 1 and 2 and evaluate improvements in classification stability and security-aware code generation.

    The work plan includes:

    (Month 1) Implement contrastive learning fine-tuning using a tailored Multiple Negatives Ranking Loss (MNRL) formulation. (Month 2) Design and integrate a security penalty term to balance false positives and false negatives. (Month 3) Analyze embedding-space separation using cosine similarity and alternative visualization techniques. (Month 4) Evaluate improvements in classification metrics (accuracy, precision, recall, F1, FNR, FPR). (Month 4) Compare fine-tuned models against baseline models without contrastive learning. (Month 5) Assess secure-by-default code generation using static analysis tools (e.g., Semgrep, CodeQL), measuring vulnerabilities per 100 lines of generated code. (Month 6) Ensure reproducibility and open-source release of training and evaluation pipelines.

    The selected candidate will be integrated into a research team with established expertise in software security, program analysis, and AI-driven code intelligence, with a track record of collaboration with leading technology companies and publications in top-tier international conferences and journals


    Contact email: bolsas@inesc-id.pt

Post-doctoral Grant

  • BPD|2026/951 Projet SHELL– refª LISBOA 2030-FEDER-00748300

    Type of position: Post-doctoral Grant
    Duration: months
    Deadline to apply: 2026-09-30
    Description

    ONE (1) Post-Doctoral Research Grant with reference number BPD|2026/951 under the scope of the Project SHELL: Serverless High-density Environment for eLastic cLouds– refª LISBOA 2030-FEDER-00748300 - 2023.16994.ICDT, co-funded by the European Union through the Lisboa 2030 Programme (ERDF) and by national funds through FCT, I.P., under project no. 16321, is now available under the following conditions:

    OBJECTIVES | FUNCTIONS 

    The candidate will integrate a research group composed of PhD and master students that work on several topics related to systems research. The candidate will be responsible for mentoring and helping junior researchers as well as developing his/her own research projects in topics related to Checkpoint/Restore applied to Serverless AI.


    Contact email: bolsas@inesc-id.pt

Contract