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Job Description

The successful candidate will work with Associate Professors Alex Thiery and Duane Loh on conducting research at the interface of Machine Learning and Bio-imaging under a project on Learning Spatiotemporal Motifs In Complex Biological Systems.

The main responsibilities of the position include:

- Conduct cutting-edge research at the intersection of AI and scientific discovery, focusing on identifying spatiotemporal motifs in complex biological cells and bio-detection platforms.

- Mentor and guide Master's level students in their research projects.

- Contribute to curriculum design, teaching, and student engagement in AI-for-Science courses and seminars.

Qualifications

Qualifications / Discipline:

- M.Sc. from a reputable institution in Physics, Bio-imaging, Computer Science, or a scientific domain closely related to Machine Learning.

- The candidate should be highly proficient with a range of machine learning approaches, including unsupervised, semi-supervised, supervised, and various deep learning techniques (e.g., convolutional neural networks, LSTMs, transformers).

- A strong foundation in statistics and strong programming skills are also required.

- Familiarity with academic teaching at the postgraduate level.

Skills:

- The candidate should be highly proficient with a range of machine learning approaches, including unsupervised, semi-supervised, supervised, and various deep learning techniques (e.g., convolutional neural networks, transformers)

- A strong foundation in statistics and strong programming skills are also required.

Experience:

- Experience working in research-intensive environments such as national research institutes or interdisciplinary laboratories.

- The candidate should be able to work effectively with principal investigators in various scientific domains, such as physics, chemistry, and biology, conveying machine learning results and collaborating to explore the insights the models have discovered.

- Participated in or helped organize university events, tech talks, or seminars, indicating familiarity with event logistics and academic engagement.

- Demonstrated ability to work with high-performance computing environments during academic training, with basic knowledge of cluster usage or cloud-based AI tools.