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Full Time Co Op Computer Science Jobs in Boston, MA

Lila Sciences

Co-Op, Autonomous SEM

Cambridge, MA · On-site

This co-op will contribute to autonomous SEM workflows that make characterization more consistent ... Computer Science, or a related technical field. * Experience building automated scientific or ...

NorthEastern

Co-op Counselor

Boston, MA

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

About the Opportunity JOB SUMMARY The Co-op Counselor supports the Office of the Chancellor and the Cooperative Education programs for the College of Social Sciences and Humanities (CSSH) and Bouve ...

Northeastern University

Co-op Counselor

Boston, MA · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

About the Opportunity JOB SUMMARY The Co-op Counselor supports the Office of the Chancellor and the Cooperative Education programs for the College of Social Sciences and Humanities (CSSH) and Bouvé ...

Keller North America, Inc.

Co-Op

Cumberland, RI

$22.85 - $26.70/hr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

We look to retain Co-Op students for multiple semesters as they grow and develop the necessary skills to succeed in the Geotechnical market with the goal to transition Co-Op students into full time ...

Northeastern University

Co-op Counselor

Boston, MA · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

About the Opportunity JOB SUMMARY The Co-op Counselor supports the Office of the Chancellor and the Cooperative Education programs for the College of Science (COS) and the College of Arts, Media and ...

NorthEastern

Co-op Counselor

Boston, MA

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

About the Opportunity JOB SUMMARY The Co-op Counselor supports the Office of the Chancellor and the Cooperative Education programs for the College of Science (COS) and the College of Arts, Media and ...

Lila Sciences

Co-op, Mechanical Engineer

Cambridge, MA · On-site

... science applications. From CAD to prototype to initial test builds, your work will directly ... Previous internship/co-op experience in industrial automation, lab automation, or R&D industries.

Lila Sciences

Co-Op, Data Extraction

Cambridge, MA · On-site

As a Data Extraction Co-Op, you will work alongside research scientists and engineers on a focused ... Pursuing a Bachelor's, Master's, or PhD in Computer Science, Chemistry, Materials Science, or a ...

Lila Sciences

Co-Op, Autonomous SEM

Cambridge, MA · On-site

This co-op will contribute to autonomous SEM workflows that make characterization more consistent ... Computer Science, or a related technical field. * Experience building automated scientific or ...

NorthEastern

Assistant/Associate Co-op Coordinator

Boston, MA

$19.75 - $27.25/hr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

This position is a full-time, non-tenure track, 12-month faculty appointment which may be ... Proficiency in computer/technology and database maintenance, with excellent time management and ...

Skyworks

RF Design Automation Co-Op

Andover, MA

$22.50 - $42/hr

You will gain valuable professional experience while exploring potential full-time career ... Computer Science. * Experience with scripting and programming languages such as Python, Cadence ...

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Full Time Co Op Computer Science information

Is a full time co op computer science a full-time job?

A full-time co-op in computer science typically involves working around 35-40 hours per week, similar to standard full-time employment. However, co-op positions are often temporary and may be part of an academic program, so the duration and expectations can vary depending on the employer and educational institution.
Full Time Co Op Computer Science jobs near you

Co-Op, Autonomous SEM

Lila Sciences

Cambridge, MA • On-site

Full-time

Re-posted 14 days ago

Job description

Your Impact at LILA

Lila Sciences is seeking a Co-Op, Autonomous SEM to join the Materials Science team within the Autonomous Science Platform. This co-op will contribute to autonomous SEM workflows that make characterization more consistent, high-throughput, and less dependent on manual operators. The work sits at the intersection of materials characterization, image analysis, and autonomous laboratory workflows. The co-op will support SEM-based imaging workflows that help instruments identify useful regions, evaluate image quality, adjust acquisition conditions, and generate datasets suitable for downstream analysis and ML training.

This is a hands-on opportunity for a student interested in building practical autonomy for scientific instruments-turning SEM from a manually driven characterization tool into a system that can navigate samples, make acquisition decisions, and produce richer datasets for materials discovery.

What You'll Be Building

  • Support development of autonomous SEM workflow using vendor APIs
  • Test navigation logic for locating particles, surfaces, and regions of interest.
  • Evaluate image quality using criteria such as focus, contrast, feature visibility, and sampling value.
  • Support experiments that connect imaging decisions to downstream analysis and ML training needs.
  • Document acquisition behavior, edge cases, and failure modes across sample types.
  • Collaborate with ML scientists, experimental scientists, and software partners on instrument-control requirements.
  • Help define practical guardrails for autonomous SEM operation, including when to capture, reposition, zoom, or adjust parameters.

What You'll Need to Succeed

  • Currently pursuing a PhD or have completed a PhD in Materials Science, Chemistry, Chemical Engineering, Physics, Applied Physics, Computer Science, or a related technical field.
  • Experience building automated scientific or laboratory workflows using Python.
  • Deep understanding of electron optics, electron-beam interaction with matter, column alignments, stigmation correction, and source dynamics
  • Familiarity with MCP servers, LLM-enabled workflows, or agentic control of scientific instruments.
  • Experience with closed-loop learning, active learning, Bayesian optimization, or reward-driven experimental workflows.
  • Ability to translate expert instrument operations into clear, testable, and well-documented workflow logic.

Bonus Points For

  • Experience with autonomous microscopy, self-driving labs, or agentic scientific workflows.
  • Experience with data analysis for scientific images, spectra, or microscopy datasets.
  • Experience with image segmentation, particle finding, feature detection, or morphology analysis.
  • Interest in building practical autonomy for scientific instruments across real sample types and workflows.

Apply