WISER Research Fellowship - AI/ML
Part-time
Location:
USA
Compensation:
Fixed monthly stipend
Job Description:
Embark on an exciting journey with WISER Solutions Launchpad that supports the journey of young talent from academia to industry. We work with leading industries and organizations and have delivered 18+ projects, partnered with 85+ organizations, and built a vibrant community of 50+ research fellows - all within just two years. We are seeking motivated researchers to join an industry-application project on AI-driven materials discovery.
We are seeking motivated researchers to join an industry-focused project at the intersection of AI, material science, and advanced energy systems.
What You'll Do:
The project establishes an AI-driven materials discovery workflow to accelerate the identification of radiation-resistant HEAs. Advanced energy applications such as nuclear reactors, naval propulsion, and space exploration demand materials that can withstand extreme radiation environments. High-entropy alloys (HEAs) have emerged as promising candidates due to their mechanical strength, corrosion resistance, and thermo-stability while under radiation. By integrating machine learning methods and computational techniques, we will design novel HEA compositions that outperform existing materials.
- Develop and apply machine learning/AI models for material discovery.
- Collaborate closely with interdisciplinary teams across academia and industry to refine use cases and research directions.
- Maintain high standards of documentation, reporting, and project communication.
- Represent the team at Conferences: Attend select industry events (limited travel) to network, present, and showcase our unique value proposition.
Who You Are:
- Background in material science, physics, applied mathematics, chemistry, computer science or related fields.
- Experience in machine learning, data science, or computational modeling.
-Strong problem solving skills and ability to work in a collaborative environment.
- You thrive in fast-paced environments and love taking ownership of ideas from concept to execution.
- Proficiency in Python, data visualization and exploratory analysis.
- Research experience in ML workflows, anomaly detection techniques, pattern recognition and pattern mining in high-dimensional data sets.
- US Citizens Only
The project establishes an AI-driven materials discovery workflow to accelerate the identification of radiation-resistant HEAs. Advanced energy applications such as nuclear reactors, naval propulsion, and space exploration demand materials that can withstand extreme radiation environments. High-entropy alloys (HEAs) have emerged as promising candidates due to their mechanical strength, corrosion resistance, and thermo-stability while under radiation. By integrating machine learning methods and computational techniques, we will design novel HEA compositions that outperform existing materials.