54 computer-programmer-"Data-driven-Materials-Modeling" Postdoctoral positions at OSU
Sort by
Refine Your Search
-
- Materials Discovery (MR) Department: Engineering | Mechanical and Aerospace Engineering Conducts research and development efforts as a Post Doctoral Scholar in the laboratory led by Professor Kang
-
OSU | about 1 month ago
modeling . A strong publication record in top-tier venues (e.g., MICCAI, CVPR, NeurIPS, AAAI, or related) Desired: Experience with multimodal data integration, longitudinal outcome prediction, or medical
-
languages such as Python or C++. Expertise in statistical analysis, machine learning, and data-driven modeling applied to large astronomical datasets and time-domain surveys is highly desirable. Although
-
fundamental Earth science and engineering applications . Research activities may involve geophysical forward modeling, AI-driven geophysical inversion, seismic monitoring and imaging, scientific machine
-
OSU | about 1 month ago
biomedical engineering, computer science, psychology, hearing science, or a related field. Applicants must demonstrate strong research skills, proficiency in data analysis and statistical methods, and
-
OSU | about 1 month ago
modeling as appropriate. Interpret findings and formulate scientifically rigorous conclusions. Collaborate with statisticians, engineers, and other specialists to support data analysis and validation
-
. Candidates should have strong scientific programming skills in modern languages such as Python or C++. Experience in statistical analysis, machine learning, and data-driven modeling applied to large
-
OSU | about 1 month ago
multiscale and hybrid modeling—integrating first-principles simulations with machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to
-
Engineering, Kinesiology, or a closely related field by the start date of the appointment. Demonstrated expertise in quantitative data analysis, including advanced statistical modeling (e.g., mixed-effects
-
advanced experimental capabilities within the research group. The experimental work is supported by first-principles based transport modeling, and the broader collaborative network includes materials