Wallenberg-NTU Postdoctoral Fellow

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.

Key Responsibilities:

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.