SDL Internship Program

The online application for the SDL internship

The Spatial Data Lab (SDL) internship program is designed to provide academic learning experience for high school (age 18 or older), undergraduate and graduate students. It offers tailored professional training based on a student’s academic or career interests, especially in the field of spatial data science. This internship will provide hands-on experience in utilizing open-source tools, workflow technology, geospatial data, spatial modeling, and their applications across different fields, including public health, business, social media, remote sensing, and environmental studies. Those outstanding interns will be invited to join the summer training workshops at Harvard and present their projects at the annual symposium organized by the Spatial Data Lab.

An internship gives a student the opportunity to experience the entire life-cycle of academic research, gain hands-on experience in cutting-edge technologies, explore career directions, develop technical skills as well as leadership abilities.

Application Cycles

Applications will be reviewed three times per year, and interviews will be scheduled for the weeks after each deadline. The application periods are as following:

  • Cycle 1-Spring
    • Application Open: October 1 – January 31
    • Interview & Selection: February 1 – February 21
    • Internship Start: March 1
    • Internship Evaluation: September
  • Cycle 2- Summer
    • Application Open: February 1 – May 31
    • Interview & Selection: June 1 – June 21
    • Internship Start: July 1
    • Internship Evaluation: January
  • Cycle 3-Winter
    • Application Open: June 1 – September 30
    • Interview & Selection: October 1 – October 21
    • Internship Start: November 1
    • Internship Evaluation: April

Internship Activities and Responsibilities:

  • Test the open-source tools for spatial data analysis developed by the Spatial Data Lab.
  • Conduct literature reviews on selected topics
  • Collaborate with the team to design/develop case studies using open-source tools, geospatial data, and spatial/GeoAI models.
  • Assist in developing curriculum and materials for teaching and training in spatial data science.
  • Support the team in collecting and organizing geospatial data, conducting data preprocessing, and ensuring data quality for geospatial training and analysis.
  • Assist in applying statistical analysis, geospatial techniques, and spatial/GeoAI models to assigned projects.
  • Contribute to the preparation of written reports and presentations on the projects.
  • Collaborate with team members and participate in discussions to provide insights, share progress, and propose innovative ideas for the projects.
  • Participating in the training workshop organized by the Spatial Data Lab.
  • Support the team in other related tasks as required.

Qualifications:

  • Currently enrolled in a high school (age 18 or older) or college/university.
  • Strong interest in geospatial analysis, workflow technology, spatial/GeoAI models, as well as their applications across different fields.
  • Knowledge of geospatial tools, such as Geographic Information Systems (GIS), remote sensing software, and programming languages (such as Python), is desirable.
  • Strong analytical and problem-solving skills to contribute to research, modeling, and GeoAI model implementation activities.
  • Good written and verbal communication skills to effectively convey ideas and contribute to team discussions.
  • Ability to work independently and as part of a team, manage time efficiently, and meet timelines.
  • Enthusiasm for acquiring new skills and knowledge in the realm of geospatial analysis and models.
  • Passion for working in a team with diverse backgrounds and different levels of technical skills.
  • Completion of the KNIME certificate program for  L1- Basic Proficiency in KNIME Analytics Platform (free).

Benefits:

  • Resource and technical support offered by the Spatial Data Lab
  • Hands-on experience working on research projects in spatial data science.
  • Network opportunity to collaborate with a diverse team of experts and professionals.
  • Coordinated mentorship and guidance from scholars and industry leaders.
  • Exposure to cutting-edge geospatial technologies and methodologies.
  • Enhanced profile for career development.
  • Possibility for co-authoring peer-reviewed papers for academic journals.
  • Signed certificate for the completion of the internship.
  • Reference for future career opportunities by project mentors.

Prerequisites

The SDL Internship Program requires participants to develop a foundational understanding of the KNIME Analytics Platform before beginning. Interns must first download KNIME and create a free account, then complete the self-paced “L1: Basic Proficiency in KNIME Analytics Platform” course. Additionally, we suggest that you watch a KNIME training video and explore KNIME’s geospatial analytics extension using the provided workshop materials.

Application:

To apply for the SDL internship, please submit the intern application, include the following points in your statement:

  • Your KNIME certificate for  L1- Basic Proficiency in KNIME Analytics Platform.
  • You understand that this will be 100% remote participation, and indicate your location (city, country is sufficient).
  • You understand that this will be non-stipend.
  • Your planned start and end dates, usually cover 3-12 months, can change later.
  • Estimated number of hours per week (on average) you will designate to SDL projects, can change during the time (such as 6-10 hr/wk).
  • Give a few examples of project tasks you could be conducting.
  • Mention some benefits to your academic development that you expect from this experience (such as learning how to conduct research, how to write peer-reviewed papers).
  • You agree to submit a short report and presentation on your project at the end, summarizing your accomplishments and learnings.
  • Your photo and short bio for the SDL website. See examples on https://gis.harvard.edu/people/affiliation/associates.
  • Your preferred mento and project.

We will review applications on a rolling basis and contact shortlisted candidates for interviews.

Please visit http://spatialdatalab.org for more details or contact spatialdatalab@lists.fas.harvard.edu for questions.