Research Intern – Tech und KI (m/w/d)

Job Description

CEEW is looking to hire a Research intern to support our newly launched CRAVIS.ai tool in calculating indicators, designing climate data and implementing the data channel.

The candidate should have a strong understanding of dealing with climate data, parallel computing, and hands-on experience in working with cloud computing, complex 3D and 4D gridded data and concentration scenarios (RCPs, SSPs) datasets. The role will involve supporting the collection, organisation, and analysis of primary and secondary data, and contributing to the upgrade of the CRAVIS.ai tool and other related tools. Additionally, the candidate will assist the core research team with data mining and cleaning, data analysis, and conducting literature reviews.

In addition to research and analysis, the candidate will be responsible for engaging with stakeholders and policymakers to provide support on outreach components for CRAVIS.ai-related work and gathering valuable insights. This involves organising workshops and collaborating with local authorities. The ideal candidate should possess an educational background in atmospheric science, climate sciences, environmental science, disaster management, or other related fields.

Key skills include experience with CDO, Python (especially numpy, xarray, xesmf, xclim), shell scripting, GIS tools, Microsoft Office, and statistical tools. Knowledge of RegCM is a plus.

In addition to the duties and responsibilities outlined above, the individual should be prepared to take on additional responsibilities as necessary.

Job Duties and Accountabilities

Primary responsibilities

Research and analysis

• Conduct rigorous research on democratising climate data Intelligence, climate risk mapping and vulnerabilities, focusing on climate, socio-economic and physical factors.
• Conduct a climate data bias correcting, statistical analysis and technical documentation.
• Support the design and implementation of CRAVIS.ai-related research and disaster vulnerability assessments in India and other identified local and state-level.
• Reviewing global climate model (CMIP6, CMIP7, etc.) literature, identify knowledge Gaps and best practices.
• Assist in developing effective methodologies for climate indicator calculation and risk assessment.
• Assist in conducting primary and secondary data collection at the Block, District, State and National levels.
• Utilise quantitative data skills, including Python, CDO, and GIS, for analysis and visualisation of research findings.
• Stay updated on emerging trends and developments in disaster management and climate resilience.
• Adapt to evolving project needs and take on additional responsibilities as required.

Other responsibilities

• Support the team and build individual capacity in strategic communications through regular blogs and op eds.
• Organise and clean field data for analysis under the guidance of the Research Analyst/Sr. Consultant.
• Assist in integrating socio-economic and climate datasets (e.g., Census, RCMs, geographical and other relevant information) to support the maintenance of cravis.ai.
• Respond to research requests from researchers, discuss research needs, and prepare responses that synthesise and analyse information.
• Assist with program implementation, including help with preparing presentations.
• Participate constructively in weekly team meetings.
• Provide inputs on research methodology and data analysis tools.

Selection Criteria

Qualification

• Preferably a Master's degree in Climate Science, Data Science, Computer Science or other related fields.

Work experience

• 0-1 years. Candidates pursuing their Bachelor's/Master's can apply.

Key skills

• A strong understanding of climate, disaster risk index, and resilience assessment frameworks, such as those provided by the IPCC and UNDRR, including components, such as hazard, risk, exposure and vulnerability.
• Sector-level comprehension of disaster management and its interaction with climate change and the impacts of climatic disasters, to reduce the loss of life and property, minimise risks, and build public awareness of human health.
• Knowledgeable Gaps in extreme events about the disaster management and the disaster governance mechanisms in India.
• Experience in mapping local-level socio-economic and physical vulnerabilities and risks through desk research and field surveys.
• Proficient in handling various datasets, such as environmental, geographical, Census, and other government-generated datasets.
• Ability to generate granular climate risk indices by integrating multiple climatological and geospatial datasets of hazards and exposure.
• Familiarity with programming languages like Unix, Python and statistical tools for data analysis.
• Understanding of disaster management and climate sciences.
• Excellent communication and collaboration skills.
• Detail-oriented approach to research and analysis.
• Adaptability and willingness to learn new skills as required.

If you are passionate about democratising climate data Intelligence that addresses local-level risks and vulnerabilities and want to contribute to advancing India, we would like to speak to you. If you can assist in mapping climate risks to develop insightful analytics while also enhancing your skills in the public policy space, you are likely the candidate we are looking for.

CEEW operates in a dynamic environment, and the candidate will be required to show flexibility in undertaking a variety of tasks.

Compensation

Competitive compensation - commensurate with the experience and matching the best of the standards adopted by industry or other similar organisations for similar roles.

Application Process

CEEW is an equal opportunity employer, and the selection process does not discriminate on the basis of age, gender, caste, ethnicity, religion, or sexuality. Female candidates are encouraged to apply. Applications will be reviewed on a rolling basis. Interested applicants are advised to apply at the earliest possible time. Only shortlisted candidates will be notified. We appreciate your interest.