Project Description:
X-ray Absorption Spectroscopy (XAS) is a powerful technique for probing the local atomic and electronic structure of materials, yet leveraging its full potential for quantitative spectral interpretation and structure inference requires overcoming significant challenges in data integration, representation, and analysis. This project contributes to a broader effort to develop a multimodal foundation model for materials characterization, inspired by ImageBind architectures, in which XAS serves as a central binding modality connecting diverse materials information, including atomic structures, spectroscopic data, and textual metadata.
The student researcher will play a key role in both developing the underlying data infrastructure and advancing the machine learning framework for multimodal learning. Project activities include: (1) assembling and preprocessing large-scale XAS datasets and associated metadata from public and internal sources; (2) designing and evaluating neural architectures for encoding and integrating heterogeneous data types such as spectra, crystal structures, and text; (3) training and benchmarking multimodal models to learn shared representations across these modalities; and (4) developing validation tasks and downstream applications, such as suggesting candidate atomic structures for XAS spectral fitting.
By jointly addressing data quality, representation learning, and model evaluation, this work aims to overcome a central bottleneck in materials informatics: the limited availability of scalable, interpretable, and transferable multimodal models for materials characterization. The project will enable the development of AI tools that enable autonomous and high-throughput XAS analysis and interpretation while providing interdisciplinary training in data science, spectroscopy, and machine learning at the interface of materials science and artificial intelligence.
Education and Experience Requirements
• The entirety of the appointment must be conducted within the United States.
• Applicants must be:
o Currently enrolled in undergraduate or graduate studies at an accredited institution.
o Graduated from an accredited institution within the past 3 months; or
o Actively enrolled in a graduate program at an accredited institution.
• Must be 18 years or older at the time the appointment begins.
• Must possess a cumulative GPA of 3.0 on a 4.0 scale.
• If accepting an offer, candidates may be required to complete pre-employment drug testing based on appointment length. All students remain subject to applicable drug testing policies.
• Must complete a satisfactory background check.
Job Family
Undergraduate Student Employment
Job Profile
Research Aide Technical - Sophomore
Worker Type
Employee
Time Type
Part time
Scheduled Weekly Hours
12
Pay Rate Type
HourlyThe expected hiring range for this position is $20.99-$28.34.
Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.
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EEO Information
As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.
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