Inesc Tec Is Accepting Applications To Award 1 Research Grant For Degree Holders

Organisation/Company INESC TEC Research Field Computer science » Informatics Researcher Profile First Stage Researcher (R1) Application Deadline 18 Nov ***:59 (UTC) Country Portugal Type of Contract Temporary Job Status Full-time Hours Per Week 36 Offer Starting Date 15 Dec Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Reference Number AE* Is the Job related to staff position within a Research Infrastructure? No
CALL FOR GRANT APPLICATIONS (AE*)
INESC TEC is now accepting grant applications to award 1 Research Grant (BI) within the scope of the project BringTrust, with reference * (COMPETE-FEDER-**) Co-funded by ERDF - European Regional Development Fund through the Innovation and Digital Transition Thematic Programme (COMPETE **) within the scope of Portugal .
1. GRANT DESCRIPTION Type of grant: Research Grant (BI)
General scientific area: COMPUTER SCIENCE
Scientific subarea: Informatics
Grant duration: 6 months 16 days, starting on ** , with the possibility of being renewed until the end of the project.
Maintenance stipend: *, according to the table of monthly maintenance stipend for FCT grants ( ), paid via bank transfer. Grant holders may be awarded potential supplements, according to a quarterly evaluation process (Articles 19, 21 and 22 of the Regulations for Grants of INESC TEC and Annex II), up to a maximum limit of 50 of the monthly maintenance stipend.
The grant holder will benefit from health insurance, supported by INESC TEC.
2. OBJECTIVES: The main objective of the work to be developed during the grant is the design and implementation of automated verification mechanisms for CI/CD workflows (Vetted Workflows) based on GitHub Actions. These mechanisms should enable the identification of vulnerabilities, misconfigurations, and untrusted components before a workflow is executed.
The work aims to integrate state-of-the-art static analysis tools, explore the use of large language models (LLMs) to detect the risks those tools do not cover and to explain the results obtained, and design the component responsible for orchestrating the different analyses and deciding whether the workflow may be executed.; Such a solution will be essential to strengthen software supply chain security, an increasingly frequent target of cyberattacks.
The results will help organizations adopt trustworthy CI/CD pipelines without compromising development agility, while ensuring the integrity of their intellectual property.;
3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: The work plan for this grant includes the following main activities:;
Integration of static analysis tools: selection and integration of state-of-the-art tools for analyzing GitHub Actions workflows into a single pipeline with a common results format, and identification of coverage gaps against the system requirements. Evaluation of the ability of LLMs to fill those gaps, comparing them with classical tools in terms of detection, false positives and cost.
Definition of the integration format and API: specification of the report format and of the interfaces through which static analysis tools, dynamic analysis tools and reputation systems exchange information.
Design and prototyping of the vetting agent: design of the component that intercepts GitHub events before execution, triggers the required analyses, collects the results and decides, through declarative policies (OPA/Rego), whether to accept or reject the workflow. Exploration of LLMs to explain that decision to developers and suggest fixes.
The tasks described in this work plan demand the application and development of concepts and techniques in the topics of distributed and operating systems, artificial intelligence, and software engineering, which are usually introduced in curricular units included in the curricula of BSc courses in Computer Science, Informatics Engineering, Biomedical Engineering and similar fields.
4. REQUIRED PROFILE: Admission requirements: BSc Degree in Biomedical Engineering or BSc Degree in Informatics Engineering
The awarding of the fellowship is dependent on the applicants' enrolment in study cycle or non-award courses of Higher Education Institutions.
Preference factors:
Knowledge of and experience with static or dynamic analysis tools.;
Knowledge of libraries for clustering and dense vector analysis (FAISS).;
Minimum requirements:
Knowledge of LLMs and RAG, as well as of deploying local LLMs.
Experience in building LLM-based recommendation systems, supported by technical evidence (such as code repositories, projects, functional prototypes, articles, or theses), including public URLs to these materials or by submitting them as attachments to the application.
Experience with CI/CD pipelines and GitHub Actions, supported by technical evidence (such as code repo