Research Staff - Federated Causal Inference
As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets that are relevant to industry demands while working on research projects in SIT.
This project focuses on federated causal inference in heterogeneous data environments, addressing the challenge of enabling trustworthy causal analysis across distributed datasets while preserving privacy. The successful candidate will be responsible for the end-to-end investigation of novel federated learning strategies for causal inference. The role will bridge rigorous theoretical work with hands-on algorithm design and development on real-world datasets. The core responsibility is to build and validate federated causal inference algorithms through simulations and live demonstrations.
Key Responsibilities
- Participate in and manage the research project with Principal Investigator (PI) to ensure all project deliverables are met.
- Derivation of novel performance metrics for federated causal inference algorithms.
- Analysis of causal inference models in federated settings using synthetic and real-world datasets.
- Design and development of novel federated causal inference algorithms and associated software APIs.
- Validation of algorithms via simulations and live demonstrations.
Job Requirements
- A Master's degree or higher in Computer Engineering, Computer Science, Data Science, Statistics, or equivalent.
- Strong theoretical background in statistics and machine learning.
- Knowledge of the basics of federated learning and causal inference is highly encouraged.
- Proven track record in research and development of machine learning algorithms.
- Proficiency in algorithm development using Python and ML frameworks such as PyTorch or TensorFlow.
Key Competencies
- Work independently, as well as within a team, to ensure proper operation and maintenance of equipment.
- Able to build and maintain strong working relationships with people within and external to the university.
- Self-directed learner who believes in continuous learning and development.
- Proficient in technical writing and presentation.
- Possess strong analytical and critical thinking skills.
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