Postdoc: Fusion Materials & Microfabrication Mass Spectrometry

Oak Ridge National Laboratory; Oak Ridge (TN); On-site; USD 60,000 - 80,000; Full time

Benefits offered by this job

Medical plans

Retirement plans

Flexible work hours

On-site amenities

Job summary

Oak Ridge National Laboratory (ORNL) seeks a postdoctoral researcher to develop separation methods using mass spectrometry, microfluidics and MEMS for hydrogen isotope separation from contaminants. The role includes analyzing irradiated ceramics and alloys for tritium technology using ToF‑SIMS, SEM, XRD, and related techniques.

The project covers data reduction of large MS and imaging datasets, vacuum instrumentation, and collaboration with ORNL teams. A strong publication record is expected.

Qualifications

• PhD in a relevant field completed within the last five years.
• Hands-on experience with advanced materials characterization equipment and data analysis tools.

Responsibilities

• Evaluate neutron irradiation effects on mechanical and thermophysical properties of ceramics and alloys.
• Characterize irradiated materials using TEM, SEM, SIMS, APT and related methods.
• Set up vacuum pumps, flow meters and microfluidic reactors for gas mixture MS studies.
• Operate ToF‑SIMS, SEM, Raman and other analytical instruments with hands‑on expertise.
• Analyze MS and microscopy data, coordinating experiments with simulations.
• Disseminate results via peer‑reviewed publications, reports, and collaborations.

Skills

Mass spectrometry

ToF-SIMS

SEM

XRD

MEMS fabrication

Vacuum technology

Education

PhD in Chemical Engineering, Materials Science & Engineering, Nuclear Engineering, or Chemistry

Tools

AP T

TEM

Raman spectrometers

Job description

Oak Ridge National Laboratory (ORNL) seeks a postdoctoral researcher to develop separation methods using mass spectrometry, microfluidics and MEMS for hydrogen isotope separation from contaminants. The role includes analyzing irradiated ceramics and alloys for tritium technology using ToF‑SIMS, SEM, XRD, and related techniques.

The project covers data reduction of large MS and imaging datasets, vacuum instrumentation, and collaboration with ORNL teams. A strong publication record is expected.