RESEARCHER - ENHANCING ATMOSPHERIC CHEMISTRY MODELING CAPABILITIES WITH MACHINE LEARNING AND OTHER CUTTING-EDGE TECHNOLOGIES (R3) - AI4S

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 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 Atmospheric Composition (AC) group within the Earth Sciences department at BSC-CNS aims at better understanding and predicting the spatiotemporal variations of atmospheric pollutants along with their effects upon air quality, weather, climate and health.
This is addressed through the continuous development and application of numerical models over multiple scales, from weather to climate and from global to urban scales.
The AC group is an international team of approximately 45 members, including researchers, engineers, and students from diverse backgrounds.
The AC group develops and maintains its own forecast model MONARCH, which is a coupled meteorological-chemistry model that can be run at global and regional scales to simulate gaseous chemistry and aerosols plus their feedbacks on radiation.
It provides operational air quality forecasts over Europe within the Copernicus Atmosphere Monitoring Services (CAMS) and dust forecasts over Africa, Middle-East and Europe in the framework of the Barcelona Dust Regional Center (BDRC).
Data assimilation is also used by the AC group to compute retrospectives analyses of the atmospheric composition or to estimate aerosol emissions.
The AC group maintains a comprehensive storage system of Earth system observations (in-situ and satellite) for model evaluation, process analysis and data assimilation.
Data curation of the storage system is achieved through harmonization and new standardized processing methodologies.
We are looking for an experienced researcher to enhance our modeling capabilities in atmospheric chemistry using cutting-edge technologies such as machine learning, high-performance computing and GPUs.
Using one or several of these key technologies, the successful candidate will propose and investigate innovative approaches for improving and/or accelerating the prediction of the atmosphere’s chemical composition.
The machine learning developments of potential interest include surrogate models for emulating all or part(s) of the MONARCH atmospheric model using state-of-the-art deep neural network architectures; and exploring avenues for improving interpretability, plausibility and physical consistency of the machine learning-based predictions.
Depending on the potential previous experience of the successful candidate on atmospheric chemistry (not mandatory but valued), some preliminary developments of interest in the MONARCH model itself will be also conducted so that to improve the treatment of the gaseous and organic aerosol chemistry.
The successful integration of these aforementioned emerging technologies into traditional atmospheric composition workflows will open new scientific avenues for better understanding and constraining the atmosphere’s chemical composition.
The successful candidate will work closely with the working group on AI established in the AC group and at the department level with participation of several members of the department, will have access to state-of-the-art systems and computational infrastructures, and will establish collaborations with experts in different areas both at the national and international levels The funding for these actions/fellowships and contracts comes from the European Union Recovery and Resilience Facility - Next Generation, within the framework of the General Invitation by the public business entity Red.
s to participate in the talent attraction and retention programs within Investment 4 of Component 19 of the Recovery, Transformation, and Resilience Plan.
For more information, please check.
https://www.
sc.
s/join-us/excellence-career-opportunities/ai4s     Key Duties   Propose and implement a strategy for integrating one or several of these cutting-edge technologies to improve our capabilities for modeling the atmosphere’s chemical composition Review the existing literature on the field and related disciplines.
Contribute in the preparation of large-scale atmospheric composition training datasets for machine learning developments Coordinate and contributing to the development of the proposed machine learning-based applications with other researchers or engineers Participate in collaborative projects with partner institutions Conduct original research and dissemination in high impact peer reviewed publications, internal and external meetings and conferences.
Provide scientific support for the preparation of competitive applications for research and/or innovation projects   Requirements   Education A PhD degree in atmospheric chemistry, physics, climate, remote sensing, environmental engineering, data science, machine learning, computer science or similar Essential Knowledge and Professional Experience Demonstrated experience in machine learning (2 years minimum) and experience with one or several Python machine learning libraries (Scikit-learn, Pytorch, Keras and/or Tensorflow) Demonstrated scientific expertise, including but not limited to a record of scholarly publications.
Good programming skills in Python or equivalent, experience with Unix/Linux and HPC environments Additional Knowledge and Professional Experience Fluency in English is essential.
Proficiency in Spanish and other European languages would be advantageous.
Expertise in atmospheric chemistry modeling is not required but will be valued Knowledge of atmospheric science data formats (GRIB, NetCDF) will be valued Experience with revision control systems (e.
., SVN or Git) will be valued Competences Ability to work in a team and in a multi-cultural environment.
Very good interpersonal skills Excellent written and verbal communication skills Ability to take initiative, prioritize and work under set deadlines Ability to work both independently and within a team Capacity to work in multidisciplinary environments   Conditions   The position will be located at BSC within the Earth Sciences Department We offer a full-time contract (37.
h/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 Duration.
4 years Holidays.
23 paid vacation days plus 24th and 31st of December per our collective agreement Salary.
€ Additional Expenses Grant.
Each fellowship will be associated with a grant for additional expenses, such as IT equipment, travel, training, stays, etc.
Starting date.
asap - the incorporation for this vacancy must be before the 16th of December   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