Application Deadline: Monday 3rd November We are seeking an intern with a strong curiosity about applying machine learning research to impactful challenges inspired by the health domain. The selected candidate will join a collaborative team of research scientists and engineers who are passionate about delivering groundbreaking machine learning technologies to the health space. This internship offers the opportunity to conduct research in machine learning for health-related applications by crafting, implementing, and evaluating innovative algorithms and models to tackle complex problems using real-world data.
Description
We are looking for individuals with a proven background in machine learning research, as proven through academic publications or equivalent experience. Ideal candidates will have a deep curiosity for solving applied challenges in healthcare using advanced ML methodologies.
Responsibilities
* Research and implement novel machine learning techniques for health-related problems and datasets.
* Contribute to the design and evaluation of models and algorithms based on the following research areas:
* Causality and causal representation learning
* Integration of scientific models with machine learning
* Physics-informed machine learning
* Structured or unstructured deep generative modelling
* Fairness and robust machine learning
* Collaborate with a team of interdisciplinary researchers and engineers.
Clearly communicate technical concepts and results across a broad technical audience.
Minimum Qualifications
* Currently enrolled in a PhD program in machine learning, data science, computer science, electrical and computer engineering, statistics, mathematics, natural sciences, or a related field.
* Proficiency in developing machine learning solutions using Python and related libraries (e.g., NumPy, pandas, JAX, PyTorch).
Preferred Qualifications
* Ability to independently drive research projects with creativity and initiative.
Strong communication skills, particularly in presenting analyses and machine learning concepts to a diverse technical audience.
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