The fellow in this research project is required to:
Literature Review & Methodology Design: Conduct a comprehensive review of state-of-the-art SSL techniques
Algorithm Development Design and implement novel regularization strategies and loss functions that leverage entropy principles to improve the stability of SSL training loops.
Diffusion Model Integration: Adapt and apply denoising diffusion probabilistic models (DDPMs) to the problem of manifold learning and data augmentation within the SSL pipeline.
Experimental Execution: Design rigorous benchmarking protocols to test the new methods against baseline algorithms using challenging, noisy industrial datasets (e.g., anomaly detection, predictive maintenance data).
Code Maintenance & Documentation: Write clean, efficient, and reproducible Python code (JAX/PyTorch/TensorFlow) and maintain technical documentation for all developed modules.
Dissemination: Prepare results for publication in high-impact machine learning conferences (e.g., NeurIPS, ICML, ICLR) and contribute to internal technical reports.
Job Requirements:
A Ph.D. in Computer Science, Machine Learning, Statistics, or a related quantitative field
Strong Programming: Python proficiency and experience with high-performance computing (HPC) clusters or cloud GPU instances.
Analytical Rigor: Ability to design ablation studies to isolate the impact of individual loss components.
Communication: Ability to clearly articulate complex mathematical concepts to an interdisciplinary team of engineers and researchers.
Proven experience in developing and debugging deep learning models using PyTorch, JAX, or TensorFlow.
Experience handling industrial or real-world datasets with limited labels.
Track record of implementing algorithms from academic papers.
Deep understanding of Semi-Supervised Learning theory, including consistency regularization, pseudo-labeling, and generative approaches.
Familiarity with Information Theory (specifically entropy concepts) as applied to model confidence calibration.
We regret that only shortlisted candidates will be notified.
Hiring Institution: NTU
Additional Information
The Wallenberg-NTU Postdoctoral Fellowship is to bring outstanding young researchers, who have graduated from a Swedish university, to NTU for two years of postdoctoral research and studies. The Postdoctoral Fellows are given the opportunity to participate in a broad range of interdisciplinary activities and programs that characterize NTU's approach to research and education.
Ntu · Ntu Main Campus, Singapore