Postdoc: In Situ Diffraction for Advanced Structural Alloys

UT-Battelle; Oak Ridge (TN); On-site; USD 70,000 - 90,000; Full time

Benefits offered by this job

Relocation Assistance

Wellness Programs

Educational Assistance

Employee Discounts

On-site fitness

Job summary

UT-Battelle's Oak Ridge National Laboratory seeks a Postdoctoral Research Associate to study mechanical behavior of advanced structural alloys using engineering diffraction. The role integrates microstructure characterization, in situ diffraction, and mechanical testing to unveil processing–microstructure–property relationships.

Ideal candidates hold a PhD in a related field and have CALPHAD software experience, materials data analytics, and machine learning expertise.

Qualifications

• PhD completed within the last 5 years.
• Experience with CALPHAD software (Thermo-Calc/DICTRA/PANDAT/FactSage).
• Proficiency in materials data analytics and machine learning techniques.

Responsibilities

• Conduct research on deformation mechanisms and mechanical behavior of advanced structural alloys.
• Perform in situ neutron diffraction experiments to study micromechanical behavior under loading.
• Characterize microstructure using SEM, EBSD, TEM, XRD and related techniques.
• Analyze lattice strain evolution, phase stress and defect evolution with advanced data tools.
• Collaborate with internal/external teams to define and advance research directions.
• Publish results in peer-reviewed journals and present findings.

Skills

CALPHAD software experience

Materials data analytics

Machine learning techniques

Education

PhD in materials science/engineering, physics, chemistry or related field

Tools

Thermo-Calc

DICTRA

PANDAT

FactSage

GSAS

GSAS-II

VDRIVE

Job description

UT-Battelle's Oak Ridge National Laboratory seeks a Postdoctoral Research Associate to study mechanical behavior of advanced structural alloys using engineering diffraction. The role integrates microstructure characterization, in situ diffraction, and mechanical testing to unveil processing–microstructure–property relationships.

Ideal candidates hold a PhD in a related field and have CALPHAD software experience, materials data analytics, and machine learning expertise.