Senior Research engineer on Statistical Methods for Analysis and Optimization of embedded Critical Systems (RE3 - T1)

This position is at Barcelona Supercomputing Center

The selection process will be fully managed by Barcelona Supercomputing Center.

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**Reference:** 304_26_CS_SDES_RE3-T1 **Job title:** Senior Research engineer on Statistical Methods for Analysis and Optimization of embedded Critical Systems (RE3 – T1) ### About BSC The Barcelona Supercomputing Center - Centro Nacional de Supercomputación (BSC-CNS) is the leading supercomputing center in Spain. It houses MareNostrum, one of the most powerful supercomputers in Europe, was a founding and hosting member of the former European HPC infrastructure PRACE (Partnership for Advanced Computing in Europe), and is now hosting entity for EuroHPC JU, the Joint Undertaking that leads large-scale investments and HPC provision in Europe. The mission of BSC is to research, develop and manage information technologies in order to facilitate scientific progress. BSC combines HPC service provision and R&D into both computer and computational science (life, earth and engineering sciences) under one roof, and currently has over 1000 staff from 60 countries. Look at the BSC experience: [BSC-CNS YouTube Channel](#) [Let's stay connected with BSC Folks!](#) We are particularly interested for this role in the strengths and lived experiences of women and underrepresented groups to help us avoid perpetuating biases and oversights in science and IT research. In instances of equal merit, the incorporation of the under-represented sex will be favoured. We promote Equity, Diversity and Inclusion, fostering an environment where each and every one of us is appreciated for who we are, regardless of our differences. If you consider that you do not meet all the requirements, we encourage you to continue applying for the job offer. We value diversity of experiences and skills, and you could bring unique perspectives to our team. ### Context And Mission The High-Performance Embedded Systems (HPES) laboratory aims at enabling the adoption of hardware, software, and artificial intelligent (AI) high-performance solutions in embedded systems as its center of gravity, but also in any system with some form of criticality, like cars, airplanes, and satellites. Our work is mainly carried out in the context of bilateral projects with several processor companies as well as several European-funded projects. For a complete list of publications of the group in the last years, please visit HPES page at the following link: The overarching objective of this position is to work in the context of several European and bilateral Projects on high-performance real-time AI-based frameworks as part of a young and dynamic team researching on computer architecture (processors and accelerators), operating system support, and statistical and AI frameworks. We are looking for a senior profile with strong experience in the application of statistical and probabilistic techniques for the analysis and optimization of embedded critical systems with focus on both software and hardware architecture designs. In particular, we are looking for a candidate with experience in Statistical and probabilistic timing analyses and use of advanced statistical techniques to support the analysis of functional and non-functional requirements in safety-critical embedded systems. The candidate is expected to have conducted her/his studies on related topics and to have a demonstrated track record of scientific publications on the same topics on top tier venues. The candidate should also have demonstrated experience in mentoring and guiding students and junior engineers, and in contributing to the state of the art of modelling and validation of complex real-time systems. This position offers the possibility to collaborate with research institutions and industry from several European locations, thus offering enriching experiences and opportunities to learn. In this respect, the candidate will work on developing project proposals for European and national research projects, as well as participate in current and upcoming research and bilateral projects as principal investigator or work package leader. ### Key Duties * Develop and apply statistical techniques to evaluate the reliability, robustness, and safety of AI and machine learning systems * Analyze system timing behavior under rare events, edge cases, distribution shifts, and adversarial or unexpected inputs * Develop data gathering techniques and tools to enable Topological Data Analysis on complex high-performant hardware in critical systems * Use methods such as Extreme Value theory, Markov’s Inequalities, Bayesian inference, probabilistic modeling, hypothesis testing, causal analysis, Monte Carlo simulation, conformal prediction, and formal/statistical verification for the analysis of real-time safety critical systems * Contribute to the research on statistical techniques to verify and improve the result of raw data gathering of AI-based control applications in real-time edge devices * Contribute to research project proposals * Advice and support master and doctoral ### Requirements * **Education** * Degree in Mathematics, Statistics or Physics * PhD in statistical analysis of execution time in high-performance computing hardware * **Essential Knowledge and Professional Experience** * Proven experience in scientific work on safety-critical systems with statistical modelling, Extreme Value Theory, and Topological data analysis * Extensive experience in the analysis and verification of real-time safety-critical systems * Statistical analysis and validation of raw hardware event data * Solid knowledge of Machine Learning Algorithms * Experience in mentoring students * Participation as Principal Investigator and Work Package leader in national or European projects * At least 3 publications in top tier real-time embedded systems conferences such as ECRTS, RTSS, ESWEEK * **Additional Knowledge and Professional Experience** * Knowledge of safety-critical systems experimental design * Familiarity with computer architecture concepts * **Competences** * Problem-solving, pro-active, result-oriented work attitude * Good communication skills including a good command of the English language (written and spoken) ### Conditions * The position will be located at BSC within the Computer Sciences Department * We offer a full-time contract (35h/week), a good working environment, a highly stimulating environment with state-of-the-art infrastructure, flexible working hours, extensive training plan, restaurant tickets, private health insurance, support to the relocation procedures * Duration: Open-ended contract due to technical and scientific activities linked to the project and budget duration * Holidays: 22 days of holidays + 6 personal days + 24th and 31st of December per our collective agreement * Salary: we offer a competitive salary commensurate with the qualifications and experience of the candidate and according to the cost of living in Barcelona * Starting date: 01.08.2026 ### Applications procedure and process All applications must be submitted via the BSC website and contain: * A full CV in English including contact details * A cover/motivation letter with a statement of interest in English, clearly specifying for which specific area and topics the applicant wishes to be considered. Additionally, two references for further contacts must be included. Applications without this document will not be considered. **Development of the recruitment process** The selection will be carried out through a competitive examination system ("Concurso-Oposición"). The recruitment process consists of two phases: * **Curriculum Analysis:** Evaluation of previous experience and/or scientific history, degree, training, and other professional information relevant to the position. - *40 points* * **Interview phase:** The highest-rated candidates at the curriculum level will be invited to the interview phase, conducted by the corresponding department and Human Resources. In this phase, technical competencies, knowledge, skills, and professional experience related to the position, as well as the required personal competencies, will be evaluated. - *60 points. A minimum of 30 points out of 60 must be obtained to be eligible for the position.* The recruitment panel will be composed of at least three people, ensuring at least 25% representation of women. In accordance with OTM-R principles, a gender-balanced recruitment panel is formed for each vacancy at the beginning of the process. After reviewing the content of the applications, the panel will begin the interviews, with at least one technical and one administrative interview. At a minimum, a personality questionnaire as well as a technical exercise will be conducted during the process. The panel will make a final decision, and all individuals who participated in the interview phase will receive feedback with details on the acceptance or rejection of their profile. At BSC, we seek continuous improvement in our recruitment processes. For any suggestions or comments/complaints about our recruitment processes, please contact recruitment [at] bsc [dot] es. For more information, please follow [this link](#). ### Deadline The vacancy will remain open until a suitable candidate has been hired. Applications will be regularly reviewed and potential candidates will be contacted. ### OTM-R principles for selection processes BSC-CNS is committed to the principles of the Code of Conduct for the Recruitment of Researchers of the European Commission and the Open, Transparent and Merit-based Recruitment principles (OTM-R). This is applied for any potential candidate in all our processes, for example by creating gender-balanced recruitment panels and recognizing career breaks etc. BSC-CNS is an equal opportunity employer committed to diversity and inclusion. We are pleased to consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or any other basis protected by applicable state or local law. For more information follow [this link](#)

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