Research Engineer, AI-enabled Fusion Plasma Diagnostics

Agency for Science, Technology and Research (A*STAR); Singapore; In loco; SGD 70.000 - 110.000; Tempo pieno

Descrizione del lavoro

The FEAT Centre, part of the Agency for Science, Technology and Research (A*STAR) in Singapore, seeks a Research Engineer to develop AI-enabled approaches for fusion plasma diagnostics and data analysis. The role spans two tracks: diagnostics/data analysis and AI model development, with flexibility to focus on one area or work across both.

You will work with plasma physicists, diagnostic scientists, and AI researchers to process heterogeneous experimental data, build scalable ML workflows, and

Competenze

• Bachelor's or Master's in Physics, Engineering, CS, ML, or related field.
• Strong background in plasma/experimental physics, data analysis, ML, or scientific computing.
• Strong numerical and data-analysis skills; proficient in Python, C/C++, or Julia.
• Experience with experimental or complex scientific datasets.
• Excellent written and spoken English; able to work in interdisciplinary teams.

Mansioni

• Develop computational methods for fusion plasma diagnostic data analysis.
• Process and analyze experimental datasets from multiple diagnostics and devices.
• Develop and evaluate ML models for diagnostic analysis, reconstruction, and forecasting.
• Create AI approaches for learning across heterogeneous data and cross-device transfer.
• Build scalable, reproducible analysis workflows using GPU/HPC resources.
• Collaborate with internal teams and international partners; contribute to publications.

Conoscenze

Plasma physics

Scientific data analysis

Machine learning

Scientific computing

Python

C/C++

Julia

English communication

Formazione

Bachelor's or Master's in Physics

Bachelor's or Master's in Engineering

Bachelor's or Master's in Computer Science

Machine Learning

Applied Mathematics

Computational Science

Strumenti

PyTorch

JAX

TensorFlow

CUDA

GPU computing

Descrizione del lavoro

Position Summary

The FEAT Centre is seeking a Research Engineer to develop AI-enabled approaches for fusion plasma diagnostics and data analysis. The work spans two closely connected areas: fusion diagnostics and data analysis, and AI model development, with flexibility to focus on either area or work across both.

Who we are looking for

The FEAT Centre is seeking a Research Engineer to contribute to the use of experimental diagnostics and AI for fusion plasma research. You will work with diverse experimental data from fusion plasma diagnostics, including microwave, imaging, and other measurement techniques, to develop new approaches for plasma analysis and interpretation. The research presents opportunities to develop AI methods that learn across heterogeneous measurements and experimental devices while incorporating knowledge of the underlying physics. This position is part of the Plasma Physics and Diagnostics Department (PPDD), which hosts expertise in plasma modelling and diagnostics. You will work closely with plasma physicists, diagnostic scientists, and AI researchers, and collaborate with international partners across universities and fusion research facilities.

The research covers two complementary technical tracks:

1. Fusion diagnostics and data analysis: Analyse and interpret experimental diagnostic data, including signal processing, data curation, diagnostic reconstruction, validation, and the development of robust analysis workflows.
2. AI model development: Develop machine-learning methods for fusion diagnostic data, including multimodal and time-series modelling, representation learning, forecasting, transfer learning, and physics-informed approaches.

What you’ll be doing

• Develop and apply computational methods for the analysis and interpretation of fusion plasma diagnostic data.
• Process, curate, and analyse experimental datasets from multiple diagnostics and fusion devices.
• Develop and evaluate machine-learning models for diagnostic analysis, reconstruction, classification, and forecasting.
• Develop and apply AI approaches for learning from heterogeneous scientific data and transferring models across fusion devices.
• Develop scalable and reproducible analysis and machine-learning workflows using GPU and high-performance computing resources.
• Collaborate with internal teams and international research partners, contribute to publications, and support collaborative projects.

What you’ll bring: Must-Haves

• Bachelor's or Master's degree in Physics, Engineering, Computer Science, Machine Learning, Applied Mathematics, Computational Science, or a closely related field.
• Strong background in at least one of the following areas: plasma or experimental physics, scientific data analysis, machine learning, or scientific computing.
• Strong numerical and data-analysis skills, with proficiency in scientific programming (e.g., Python, C/C++, Julia).
• Experience working with experimental, simulation, or other complex scientific datasets.
• Strong written and spoken English communication skills, with the ability to work effectively in interdisciplinary teams.

Good-to-Haves

• Background in plasma physics, fusion science, plasma diagnostics, or experimental physics.
• Experience with diagnostic or scientific data analysis, including signal processing, time-series analysis, imaging, or inverse problems.
• Experience developing machine-learning models using frameworks such as PyTorch, JAX, or TensorFlow.
• Experience with areas such as multimodal learning, representation learning, self-supervised learning, foundation models, forecasting, transfer learning, or uncertainty quantification.
• Experience with scientific or physics-informed machine learning.
• Experience with high-performance computing, GPU acceleration, or large-scale computational workflows.

What you’ll need to succeed

• Scientific problem-solving and critical thinking
• Initiative and willingness to learn
• Research communication (written and oral)
• Collaboration and teamwork in interdisciplinary environments

What we offer

• Opportunity to develop AI and diagnostic methods for challenging problems in fusion plasma research.
• Access to experimental datasets from international fusion research programmes and advanced GPU and high-performance computing resources.
• Close collaboration with plasma physicists, diagnostic scientists, AI researchers, and international fusion research partners.
• Flexibility to pursue research in fusion diagnostics and data analysis, AI model development, or across both areas.