🚀 Internship

Internship Opportunity: Causal Machine Learning Research Internship (Cambridge, UK)


2mo ago

🚀 Off-cycle


Rolling basis


Research Internships at Microsoft provide a dynamic environment for research careers with a network of world-class research labs led by globally-recognized scientists and engineers. Our researchers and engineers pursue innovation in various scientific and technical disciplines to help solve complex challenges in diverse fields. 

Causal machine learning is essential for decision-making in many domains, such as business decision-making. In Microsoft Research, we push technology boundaries in scalable causal methods by inventing novel models utilizing deep learning for challenging applications. 

We are looking for a highly motivated intern to work on innovative causal machine learning in causal machine learning for summer/fall 2023. The candidate will closely work with researchers of the Causal AI team. The research will involve advancing state-of-the-art causal machine learning methods, including but not limited to causal discovery, causal inference, and causal deep learning for real-world applications in the context of both first-party and third-party collaborations. You will have the opportunity to collaborate with both researchers and engineers in Microsoft research and with the protential to experience how advanced research can make a huge impact in the real-world.   

There is no closing deadline for this post. The post will be filled once suitable candidates are found so if you are interested please apply as soon as possible. When submitting your application, include your CV with a list of publications as an attachment. You may also include your published work. 

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Area of Responsibilities

Research & Development


Design, implementation, and experimental validation of causal machine learning models and algorithms. Collaboration with other researchers and engineers as part of a project team. Clearly communicating research ideas and results in writing, such as research papers, presentations, or research notes for internal and external audiences. 
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Must be accepted or currently enrolled in a PhD program in CS, EE or a related STEM field. Understanding and demonstrated research track record of causal machine learning and related fields such as structure discovery, probabilistic deep learning, through active research in a related PhD program or publications in academic conferences (NeurIPS, ICML, ICLR), journals, or workshops. . Demonstrated ability to write code, programming abilities in Python, comfortable with using Git and GitHub processes such as code reviews, and software testing principles. Experience in building machine learning systems with PyTorch. 
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