Researcher - Machine Learning models for the prediction of physicochemical properties of sustainable transportation fuels (RE2) Closing Date: Thursday, 21 November, 2024
Reference: 791_24_CASE_PTG_RE2
Job title: Researcher - Machine Learning models for the prediction of physicochemical properties of sustainable transportation fuels (RE2)
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, and is now hosting entity for EuroHPC JU. The mission of BSC is to research, develop and manage information technologies to facilitate scientific progress.
Context And Mission Considerable effort is put into making renewable energies a practical technology in the transportation sector. The applicant will lead the development and implementation of a ML algorithm for predicting fuel mixture properties, aiming at efficiently predicting fuel properties for common gasoline-oxygenated fuels and kerosene mixtures.
Key Duties Collaborate with different project partners to carry out collaborative research. Lead the development and implementation of AI/ML methods for the calculation of fuel properties. Define the input/output structure of the proposed network and associated database. Identify physical laws and correlations for the mentioned target properties. Requirements Education: PhD Degree in Computer Science, Physics, Mechanical Engineering, or Aerospace. A solid background in data-driven methods and Artificial Intelligence. Essential Knowledge and Professional Experience: Knowledge of fluid mechanics and thermodynamics. Additional Knowledge and Professional Experience: General knowledge of programming languages such as Fortran, Python, C, and C++. Fluency in English is essential; Spanish is welcome. Background in computer science, GPU programming, and HPC with a focus on Python programming is an asset. Competences: Strong analytical skills. Ability to work independently and within a team. Good communication and teamwork skills. Conditions Full-time contract (37.5h/week) with a good working environment and flexible working hours. Open-ended contract linked to project duration. 23 paid vacation days plus additional holidays. Competitive salary based on qualifications and experience. Applications procedure and process All applications must be made through the BSC website and contain:
A full CV in English including contact details. A Cover Letter with a statement of interest in English, including two contacts for references. Deadline The vacancy will remain open until a suitable candidate has been hired. Applications will be regularly reviewed.
OTM-R principles for selection processes BSC-CNS is committed to diversity and inclusion. We welcome all qualified applicants for employment without regard to any basis protected by applicable law.
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