Researcher - Modelling Tools For Climate And Health (R2) -Ai4S

Job Reference 579_24_ES_GHR_R2 Position Researcher - Modelling Tools for Climate and Health (R2) -AI4S Closing Date Monday, 16 September, 2024 Reference: 579_24_ES_GHR_R2 Job title: Researcher - Modelling Tools for Climate and Health (R2) -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 1000 staff from 60 countries.
Context And Mission The Global Health Resilience (GHR) group led by ICREA Research Professor Rachel Lowe at the BSC-CNS is seeking a highly motivated postdoctoral scientist to conduct cutting-edge methodological research on tracking past, present and future health risks posed by climate change, environmental degradation, human movement and other global change processes.
The mission of the GHR group is to apply a transdisciplinary approach to co-designing policy-relevant methodological solutions, to enhance surveillance, preparedness and response to climate-sensitive disease outbreaks and health outcomes.
The group conducts cutting-edge methodological research on disentangling the impacts of global environmental change on infectious disease risk and co-developing impact-based forecasting models at sub-seasonal to decadal timescales in collaboration with public health, disaster risk management, and humanitarian agencies.
The GHR group works closely with the Earth System Services (ESS) group, whose mission is to research the impact of weather, atmospheric chemistry and climate upon socio-economic sectors, including renewable energy, agriculture, water management, forest fires, urban development and health and demonstrate the ongoing value of earth system services to society and the economy.
The selected candidate will co-develop innovative modelling ensembles using statistical, machine learning and mathematical modelling approaches, informed by a combination of open-source and decision-maker provided data.
These innovative modelling frameworks will feed into decision-support toolkits at multiple spatial scales and different forecasting horizons for a diverse set of users.
The candidate will interact with international partners of projects funded by the Wellcome Trust and Horizon Europe, covering topics including early warning and response systems, health impact attribution, and digital twins.
The candidates will benefit from interdisciplinary training opportunities tailored to their experience and interests.
The research will be positioned within the context of WMO's Global Framework for Climate Services (GFCS), whose aim is to provide actionable climate information to key sectors of society.
This position presents an opportunity to work alongside a wide range of leading international climate and health scientists delivering cutting-edge climate services for the health sector to inform policy makers in Latin America and the Caribbean, Europe, and worldwide.
Successful candidates will benefit from expert training and BSC-CNS staff benefits: international multidisciplinary scientific environment and advanced applied research training.
We encourage applications from highly motivated candidates with demonstrated experience in impact modelling for public health and an interest in applied research in the context of climate and environmental change.
Key Duties Co-create decision support systems to enhance public health resilience to climate change Harmonise multi-source, multi-scale climate, land-type, demographic, socio-economic, human movement and health datasets Formulate statistical, machine learning and mathematical models to understand the impact of environmental change and socio-economic factors on infectious disease risk and health outcomes Develop indicators to track the impact of climate change on infectious disease risks and health outcomes Incorporate aspects of inequalities, inequities and injustices in quantitative research Disseminate research outputs in peer-reviewed scientific papers and international conferences Interact with scientists in the group, department, center and other institutions in Barcelona to enhance synergies Apply for competitive grants and projects Engage with local communities, stakeholders and policy makers Develop training materials and deliver training workshops to researchers, data managers and stakeholders Coordinate core activities of the global health resilience group Support administrative duties of the group, including arranging meetings, taking minutes, writing deliverable and mission reports, maintaining Wiki/webpages, etc.
Co-supervise Masters students Lead on policy engagement and contribute to country profiles, policy report and policy briefs Requirements Education BSc and MSc in Epidemiology, Biology, Ecology, Meteorology, Environmental Sciences, Geography, Physics, Mathematics, Statistics (or equivalent) Essential Knowledge and Professional Experience PhD in infectious disease epidemiology, environmental epidemiology, statistical, ecological or environmental modelling or related field Experience in data management and statistical analyses Proficiency in scientific programming in a suitable language (e.g., R, Python) and experience with git Experience using data visualization packages, mapping tools and dashboard creation (e.g., Shiny, etc.)
Excellent written and verbal communication skills in English, demonstrated in scientific publications and well documented repositories Ability to work in a collaborative professional environment within a transdisciplinary and international team Additional Knowledge and Professional Experience Fluency in English is essential.
Proficiency in Spanish and other languages would be advantageous Experience with data from the field of climate and environmental sciences Experience in the processing and evaluation of satellite/drone images and geodata (e.g., GIS) Experience working with public health stakeholders and international agencies Knowledge on some of the following topics: Bayesian statistics, machine learning, spatial epidemiology, infectious disease transmission modelling, human movement modelling, statistical downscaling, multi-model combination techniques, and bias adjustments techniques Competences Ability to work in a team and in a multi-cultural environment Problem-solving, pro-active, result-oriented work attitude Excellent communication skills Ability to show initiative, prioritize tasks and meet deadlines Ability to learn and adapt to multiple programming languages easily Conditions The position will be located at BSC within the Earth Sciences Department We offer a full-time contract (37.5h/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: 45.00,00€ 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 2024 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.
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.
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.
The recruitment panel will be composed of at least three people, ensuring at least 25% representation of women.
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.
This position is reserved for candidates who meet the requirements and have the legal status of disabled persons with a degree of disability equal to or greater than 33%.
In case there are no applicants with disabilities that meet the requirements, the rest of the candidates without declared disability will be evaluated.
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