Positions: 7
Research Grant (BII)
BII|2026/926-927-928-929-930-ACHILLES
Type of position: Research Grant (BII)
Duration: 3 months
Deadline to apply: 2026-06-16
DescriptionFIVE (5) Research Initiation Grants for students enrolled in a BSc, Master or Non-degree programme with references number BII|2026/926 - BII|2026/927 - BII|2026/928 - BII|2026/929 - BII|2026/930, under the scope of the Project ACHILLES with the refª 101189689, funded by European Commission, Program HORIZON EUROPE, are now available under the following conditions:
OBJECTIVES | FUNCTIONS
The main goals are: 1) to experimentally study the current limitations state-of-the-art systems for OS-level page placement on multi-tiered memory systems when used to support AI workloads; and 2) based on such results, design, implement and evaluate improvements to such systems, with a focus on
the efficiency of the page promotion mechanisms; the accuracy of the hotness classification mechanisms; bandwidth-aware rebalancing mechanisms.The work will rely on designs such as TPP, Chrono, MEMTIS, as well as the Ambix/ATLAS system designed by our team at INESC-ID.
It will target latest-generation server architectures with CXL-based memory (either local or remote/shared) as a slow memory tier, using machines from our cluster at DPSS/INESC-ID.
The obtained results are expected to be presented in at least 2 scientific papers experimental chapter, to be submitted for publication at relevant international scientific conferences.
Contact email: bolsas@inesc-id.pt
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
DescriptionONE (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 typeAnalyze 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.ptBI|2026/882 Projet SALVE – refª 2024.14936.PEX
Type of position: Research Grant (BI)
Duration: 6 months
Deadline to apply: 2026-12-31
DescriptionONE (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 vulnerabilitiesAll 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.ptBI|2026/881 Projet SALVE – refª 2024.14936.PEX
Type of position: Research Grant (BI)
Duration: 6 months
Deadline to apply: 2026-09-30
DescriptionONE (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.ptProj. SEQURED BI|2026/923 - BI|2026/924
Type of position: Research Grant (BI)
Duration: 3 months
Deadline to apply: 2026-06-12
DescriptionTWO (2) research grants for students with BSc degree with reference numbers BI|2026/923 - BI|2026/924, Project SEQURED - Refª 101168112, funded by European Commission - Program HORIZON EUROPE (European Defence Fund), is now available under the following conditions:
OBJECTIVES | FUNCTIONS
Development and implementation of Post Quantum Cryptography algorithms.
Contact email: bolsas@inesc-id.ptBI|2026/925 Project VERSACOMP
Type of position: Research Grant (BI)
Duration: 3 months
Deadline to apply: 2026-06-08
DescriptionONE (1) research grant for students with MSc degree with reference number BI|2026/925 under the scope of the Project VERSACOMP, is now available under the following conditions:
OBJECTIVES | FUNCTIONS
This scholarship recipient is expected to perform the following tasks:
Interdisciplinary investigation of high-performance computing applications that leverage matrix processing units, assessing the operations and numerical formats used, and the targeted hardware. Identification and characterization of computation-intensive phases in scientific and engineering workflows that are amenable to acceleration through the efficient use of matrix multiplication units. Write a technical report about the performed work.
Contact email: bolsas@inesc-id.pt
Contract
Public notice for one uncertain-term work contract for a Researcher reference 2026.006.CTTRI
Type of position: Contract
Duration: months
Deadline to apply: 2026-06-08
Description
The selected researcher will coordinate the work of the work package dedicated to the development of the technical and operational framework of the project (WP3). To conduct research on the creation of AI agents for human-robot interaction scenarios aligned with the project use cases.
Contact email: contratos@inesc-id.pt