University of South Carolina PhD computer science (Deep Learning and Image Processing)
PhD & Postdoctoral Positions Available- Deep Learning or Image Processing
The University of South Carolina is inviting applications for several Ph.D. positions in Mechanical Engineering for the Spring and Fall 2024 terms, as well as immediate openings for postdoctoral roles within Dr. Yi Wang’s research group.
The university is renowned as South Carolina’s premier institution, with the Mechanical Engineering department boasting a national ranking of No. 31 by the National Research Council (NRC). The College of Engineering and Computing stands out as the state’s leader in faculty research productivity.
PhD scholarship – Deep learning image analysis
Dr. Wang’s team is at the forefront of computational and data-enabled science and engineering (CDS&E), applying its principles to complex, real-world systems such as micro/nanofluidics, energy management, and additive manufacturing. CDS&E is a burgeoning field that intersects with every stage of technological and industrial engineering development, from initial concept to automation, control, and final verification and validation (V&V).
The group’s mission is to pioneer new methods, frameworks, and capabilities that integrate CDS&E with system engineering, with a special focus on multiphysics and engineering intelligence.
Requirement:
We are on the lookout for driven candidates with a background in applied mathematics, mechanical engineering, electrical engineering, or chemical engineering. Ideal applicants will have a robust foundation and practical experience in numerical modeling, high-performance computing (including CFD and FEM), machine learning, data mining, and system control within domains such as fluid dynamics, energy systems, and additive manufacturing.
Modeling & Computation & Control
1. Reduced Order Modeling, Machine Learning, and Design Optimization for Multiphysics Engineering Systems
Our research endeavors will focus on creating and refining reduced order modeling, machine learning, and design optimization techniques for a variety of engineering applications. These applications span across thermal-fluidics, aerospace, aeroservoelasticity, energy materials, additive manufacturing, and the study of microfluidics & nanofluidics.
2. Real-time Computing and Control on Edge Computing
We will pioneer the development of real-time control frameworks, algorithms, and cyber-physical systems tailored for engineering applications on edge computing platforms. These applications encompass energy auditing & cybersecurity, multi-fidelity model optimization, and autonomous systems, including real-time fault detection, mitigation, path planning, and control.
Our research will involve one or more of the following areas:
– Crafting reduced order models for complex engineering systems
– Developing data mining, machine learning, and optimization algorithms
– Creating CPU+GPU computing algorithms
– Constructing control frameworks for various robotic platforms
Preferred qualifications for candidates include:
– A solid foundation in control theory, linear algebra, computational mathematics, and/or mechanics
– Experience in developing numerical models, codes, and computation algorithms (CFD and FEM)
– Practical skills with computing in Matlab, C/C++, Python, or other object-oriented programming languages
– Experience with embedded systems and edge-computing modules, such as Jetson TX series, Intel NUC, Raspberry Pi, and Arduino
– A strong drive and self-motivation to engage in groundbreaking research, overcome real-world engineering challenges, and publish influential papers
Eligibility
To join our dynamic team, please compile your CV/Resume, list of publications, and other relevant documents into a single PDF file. Ph.D. candidates should also include transcripts and GRE scores. Submit your application to Dr. Wang at yiwang@cec.sc.edu, with the subject line “Position Application”.
