Publisher
source

Haifeng Wang

3 months ago

PhD Position in Scientific Machine Learning, Surrogate Modeling & Optimization Washington State University in United States

Degree Level

PhD

Field of study

Computer Science

Funding

Available

Deadline

Expired

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Country

United States

University

Washington State University

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Where to contact

Official Email

Keywords

Computer Science
Machine Learning
Mathematics
Computer Vision
Disaster Resilience
Surrogate Modeling
Python
Optimization

About this position

A PhD position is available in the research group of Assistant Professor Haifeng Wang at Washington State University, focusing on Scientific Machine Learning, Surrogate Modeling, and Optimization for complex engineering systems. The group’s research interests include developing advanced surrogate models and optimization algorithms, with applications in areas such as computer vision and hazard resilience. Candidates should be passionate about mathematics and capable of implementing algorithms from scratch, demonstrating strong technical skills in Python programming.

Applicants are required to complete a preliminary coding challenge, which serves as a filter for technical fit. The challenge instructions can be downloaded from the provided link. The application must include a CV, transcripts, and coding solution files (.pdf and .ipynb), and be submitted via email to [email protected] following the strict subject line format. Applications that do not adhere to these instructions will not be considered.

This opportunity is ideal for students interested in scientific machine learning, mathematical modeling, and optimization within engineering contexts. The position is based at Washington State University in the United States. Funding details are not specified in the announcement. For more information about the supervisor, see the provided LinkedIn profile.

Funding details

Available

What's required

Applicants must have strong skills in mathematics and Python programming, with the ability to implement algorithms from scratch. Candidates should be passionate about scientific machine learning and optimization. Submission of a completed coding challenge, CV, transcripts, and coding solution files (.pdf and .ipynb) is required. Applications must follow the specified subject line format. No explicit mention of degree background, but technical fit is emphasized.

How to apply

Download the coding challenge PDF from the provided link. Solve the task using Python. Email your application, including CV, transcripts, and coding solution files (.pdf and .ipynb), to [email protected] with the specified subject line format. Incomplete or incorrectly formatted applications will not be reviewed.

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