Positions: 15

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|2026/881 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

  • BI|2026/879 Projet SALVE – refª 2024.14936.PEX

    Type of position: Research Grant (BI)
    Duration: 6 months
    Deadline to apply: 2026-04-08
    Description

    ONE (1) research grant for students with MSc degree with reference number BI|2026/879 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 establishes the foundational dataset and perturbation framework for the project. The PhD student will refine, formalize, and extend an existing perturbation methodology to construct a principled, security-aware dataset of real-world vulnerable and secure JavaScript code. The work plan includes:

    1. Systematic Mining of Real-World Vulnerabilities (Month 1)

    - Extract vulnerable and patched JavaScript code from open-source repositories and vulnerability databases, leveraging and extending the team’s existing vulnerability-mining tooling to ensure precise commit-level alignment and reproducibility.

    2. Formalization and Extension of Security-Preserving Perturbations (Month 2-3)

    - Collect/define and systematize existing perturbation strategies.

    - Define transformation classes that preserve both semantic behavior and security properties.

    - Design a principled perturbation taxonomy covering:

    Syntax-level variations Control-flow transformations API-level substitutions Obfuscation and encoding strategies

    - Identify and resolve transformation edge cases that may alter security properties.

    3. Design of a Reproducible Perturbation Framework (Month 4-5)

    Implement AST-based source-to-source transformations.

    Ensure extensibility and modularity of the perturbation engine.

    Provide formal documentation of transformation operators and constraints.

    Establish reproducible pipelines for dataset generation.

    4. Quality Assurance and Dataset Standardization (Month 5-6)

    - Validate that perturbed samples preserve original vulnerability status.

    - Remove transformations that inadvertently introduce or remove vulnerabilities.

    - Structure and document the dataset to serve as the canonical foundation for subsequent project tasks.

    This task will result in a rigorously designed and extensible perturbation framework and a high-quality curated dataset forming the basis for later validation and robustness studies. 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|2026/876 - Project GREENCHIPS - Refª 101123309

    Type of position: Research Grant (BI)
    Duration: 6 months
    Deadline to apply: 2026-03-30
    Description

    ONE (1) research grants for students with MSc degree with reference number BI|2026/876, project GREENCHIPS - Refª 101123309 - 2022-SKILLS-03, funded by European Commission - Program HORIZON EUROPE, is now available under the following conditions:

    OBJECTIVES | FUNCTIONS 

    Design of a digital, capless, low dropout linear regulator (LDO).

    -Study of the state of the art of digital LDO

    -Study of the state of the art of capless LDOs

    -Design of a capless, digital LDO schematic

    -Design the corresponding layout


    Contact email: bolsas@inesc-id.pt

  • BI|2026/875-Project GREENCHIPS - Refª 101123309

    Type of position: Research Grant (BI)
    Duration: 6 months
    Deadline to apply: 2026-03-30
    Description

    ONE (1) research grants for students with BSc degree with reference number BI|2026/875, project GREENCHIPS - Refª 101123309 - 2022-SKILLS-03, funded by European Commission - Program HORIZON EUROPE, is now available under the following conditions:

    OBJECTIVES | FUNCTIONS 

    Design the control of the OTP (One Time Programmable) memory:

    - Control logic must be able to write the OTP memory

    - Control logic must be able to read OTP memory and write its contend in the register bank

    OTP control logic must be configurable to accommodate different OTP sizes (number of addresses and number of bits per address)


    Contact email: bolsas@inesc-id.pt

  • BI|2026/877 - Project GREENCHIPS - Refª 101123309

    Type of position: Research Grant (BI)
    Duration: 6 months
    Deadline to apply: 2026-03-30
    Description

    ONE (1) research grants for students with BSc degree with reference number BI|2026/877, project GREENCHIPS - Refª 101123309 - 2022-SKILLS-03, funded by European Commission - Program HORIZON EUROPE, is now available under the following conditions:

    OBJECTIVES | FUNCTIONS 

    Instrument Driver Development: Development of custom drivers to interface benchtop test equipment with an automated control ecosystem. Responsibilities include the implementation of robust, device-specific drivers that translate standardized class commands into precise low-level execution commands using standard protocols like SCPI. Goal is to ensure seamless hardware interoperability, effectively bridging the gap between high-level automation scripts and physical hardware control.


    Contact email: bolsas@inesc-id.pt

  • BI|2026/878 - Project GREENCHIPS - Refª 101123309

    Type of position: Research Grant (BI)
    Duration: 6 months
    Deadline to apply: 2026-03-30
    Description

    ONE (1) research grants for students with BSc degree with reference number BI|2026/878, project GREENCHIPS - Refª 101123309 - 2022-SKILLS-03, funded by European Commission - Program HORIZON EUROPE, is now available under the following conditions:

    OBJECTIVES | FUNCTIONS 

    Design of Hibrid Analog to Digital Converter reconfigurable as Sucessive Aproximation and Sigma Delta topologies

    1-  Study of the state of the art of Analog to Digital Converter, namely Sucessive Aproximation and Sigma Delta topologies

    2-  Schematic design of the reconfigurable ADC

    3-  Schematic validation is corners and Monte Carlo analysis

    4-  Layout design of the ADC


    Contact email: bolsas@inesc-id.pt

  • BI|2026/874-Project GREENCHIPS

    Type of position: Research Grant (BI)
    Duration: 6 months
    Deadline to apply: 2026-03-30
    Description

    ONE (1) research grants for students with BSc degree with reference number BI|2026/874, project GREENCHIPS - Refª 101123309 - 2022-SKILLS-03, funded by European Commission - Program HORIZON EUROPE, is now available under the following conditions:

    OBJECTIVES | FUNCTIONS 

    Automate the design of a register bank and the corresponding interface with I2C.

    The register bank as a variable number of addresses and it must be automatically designed in Verilog with the purpose of being synthesized.

    The I2C interface operates at a different clock frequency and must be able to read and write the register bank. 

    Additional hw (like an ADC) can also write the register bank.


    Contact email: bolsas@inesc-id.pt

  • 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|2026/873-Project U2DEMO - Refª 101160684

    Type of position: Research Grant (BI)
    Duration: 6 months
    Deadline to apply: 2026-03-26
    Description

    ONE (1) research grants for students with BSc degree with reference number BI|2026/873, project U2DEMO - Refª 101160684, funded by European Commission - Program HORIZON EUROPE, is now available under the following conditions:

    OBJECTIVES | FUNCTIONS 

    Develop optimal scheduling strategies for energy communities with flexible assets. Propose and develop decision support methods for energy communities to participate in multiple energy markets. Evaluate the impact of informed decision-making approaches vs non-informed decision-making approaches. Test the developed algorithms in Lab/real environment.


    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

Contract