Research Engineer (Federated Causal Inference in Heterogeneous Data Environments) - UP

SINGAPORE INSTITUTE OF TECHNOLOGY (SIT); Singapore; On-site; SGD 60,000 - 80,000; Full time

Job summary

A leading university in Singapore is seeking a Researcher for federated causal inference projects. The role involves designing algorithms and managing research projects. Applicants should have a Master's degree in a relevant field and a strong background in statistics and machine learning. This full-time position promises an engaging research environment and the opportunity to work closely with industry.

Qualifications

• A Master's degree or higher in Computer Engineering, Computer Science, Data Science, or equivalent.
• Strong theoretical background in statistics and machine learning.
• Proficiency in algorithm development using Python.

Responsibilities

• Participate in and manage the research project with Principal Investigator to ensure project deliverables.
• Derivation of performance metrics for federated causal inference algorithms.
• Design and development of novel federated causal inference algorithms.

Skills

Strong theoretical background in statistics

Machine learning

Algorithm development using Python

Critical thinking

Education

Master's degree or higher in Computer Engineering or related fields

Tools

PyTorch

TensorFlow

Job description

Organisation/Company SINGAPORE INSTITUTE OF TECHNOLOGY (SIT) Research Field Engineering Computer science Researcher Profile Recognised Researcher (R2) First Stage Researcher (R1) Country Singapore Application Deadline 13 Nov 2025 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

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.
• 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.