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The Human Resilience Technology Group develops sensors, analytics, and decision support software to build technologies and systems that improve human resilience by aiding in wildfire response coordination, urban search-and-rescue missions, damage assessments after hurricanes and earthquakes, and large-scale evacuation planning. We work closely with local, state, and federal agencies to develop these technologies. Our goal is to enable organizations to more effectively gain situational awareness, share information, and make decisions in times of crisis. We also prototype technologies that facilitate civilian–military collaboration, which helps to improve the safety of civilians in conflict areas. Many of our programs involve collaboration across Laboratory groups whose technologies can be applied to human resilience missions.

Recent program efforts have included:

+ Integrating advanced remote sensing and situational awareness technologies in large-scale disaster relief and recovery needs

+ Applying novel analytical methods – including artificial intelligence and machine learning – to longstanding humanitarian challenges, especially in sparse and challenging data environments

+ Creating software decision support and data visualization platforms for critical decision-making

Position Description

The Human Resilience Technology Group is seeking a co-op student to support research programs in neural operators and physics-based machine learning. We welcome applicants with backgrounds in computer science, physics, climate science, or related fields.

This position will focus on the development of machine learning techniques in computer vision, physics-informed ML, and geophysics. Students may also contribute to peer-reviewed academic publications.

Eligibility:

Applicants ideally are graduate students; or undergraduate academic juniors or above with research experience.

Required Qualifications:

+ Familiarity with Python and geospatial/ML libraries (PyTorch, pdal, pointcept)

+ Comfort working with remote sensing data

+ Experience with HPC Environments

+ Strong teamwork skills for collaboration on a multidisciplinary team

+ Ability to identify technology needs, prototype solutions, and quantitatively evaluate performance

Preferred Qualifications:

+ Familiarity with Numerical Simulations of PDEs

+ Familiarity with Containerization

Compensation

Compensation for 2025 (will be reviewed for 2026)

+ Technical Co-Op: $24.50 – $31.00 per hour (based on year in school)

+ Administrative Co-Op: $21.50 – $25.00 per hour (based on year in school)

Selected candidate will be subject to a pre-employment background investigation and must be able to obtain and maintain a Secret level DoD security clearance.

MIT Lincoln Laboratory is an Equal Employment Opportunity (EEO) employer. All qualified applicants will receive consideration for employment and will not be discriminated against on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, veteran status, disability status, or genetic information; U.S. citizenship is required.

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