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The Institut Barcelona d’Estudis Internacionals (IBEI) invites applications for one (1) Research Assistant position to work in the research project "Representative Government Through Democratic Governance (REPGOV)", funded by the European Research Council (ERC). We are currently seeking a detail-oriented Research Assistant to contribute to essential data wrangling tasks, particularly in handling scraped data that is predominantly stored in XML and JSONL formats. Specifically, the Research Assistant will perform tasks including the following.

• Parse scraped data
• Extract pertinent information from XML and source code (JSONL).
• Validate and ensure the accuracy of data extraction to facilitate seamless downstream analysis.
• Implement various reality checks to enhance data quality and integrity.
• Clean parsed data
• Employ text processing techniques to clean and refine extracted data.
• Implement various reality checks to enhance data quality and integrity.

Qualifications for the position include:

• Strong data processing skills;
• Intermediate knowledge of Python for data analysis;
• Advanced knowledge of Pandas and associated libraries;
• Experience in handling complex datasets;
• Experience parsing text efficiently and extracting information from textual data;
• Proficiency in working with XML and JSONL formats;
• While not required, knowledge of R is a plus.

The position requires a time commitment of 20 hours/week for two months (extendable to three months if necessary). While onboarding will take place in person and on-site in our IBEI offices, some subsequent tasks can be performed remotely. The successful candidate does not need to be based in Barcelona. The monetary stipend is commensurate with that for similar positions at IBEI.

Review of applications will begin on 12 February 2024 and will continue until the position is filled.  

🖊️ Applications must be submitted through the Application Form and it should include:

• A motivation letter that includes information about experience with data cleaning and working with large datasets (1 page).
• The applicant’s current CV including the names of two references.

Grant number: 101020966