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Job ID: 259919

Postdoctoral Scholars – Multi-Disciplinary (2025)
University of California, San Diego

The Office of Research Affairs, at the University of California, San Diego, in support of the campus, multidisciplinary Organized Research Units (ORUs) https://research.ucsd.edu/ORU/index.html is conducting an open search for Postdoctoral Scholars in various academic disciplines. The University of California employs about 6,000 postdoctoral scholars (approx. 1,200 at UCSD) who contribute to the academic community by enhancing the research and education programs of the university.

The postdoctoral experience emphasizes scholarship and continued research training. UC’s postdoctoral scholars bring expertise and creativity that enrich the research environment for all members of the UC community, including graduate and undergraduate students. Postdocs are often expected to complete research objectives, publishing results, and may support and/or contribute expertise to writing grant applications. Appointment durations vary depending

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Artificial intelligence has transformed fields like medicine and finance, but it hasn’t gained much traction in manufacturing. Factories present a different challenge for AI: They are structured, fast-paced environments that rely on precision and critical timing. Success requires more than powerful algorithms; it demands deep, real-time understanding of complex systems, equipment and workflow. A new AI model designed specifically for manufacturing, seeks to address this challenge and revolutionize how factories operate.

With support from the U.S. National Science Foundation, a team led by California State University Northridge’s Autonomy Research Center for STEAHM has developed MaVila — short for Manufacturing, Vision and Language — an intelligent assistant that combines image analysis and natural language processing to help manufacturers detect problems, suggest improvements and communicate with machines in real time. Their goal is to create smarter, more adaptive manufacturing systems that can better support one of the most important sectors of the U.S. economy.

MaVila takes a different approach. Instead of relying on outside data, like information on the internet, it is trained with manufacturing-specific knowledge from the start. It learns directly from visual and language-based data in factory settings. The tool can “see” and “talk” — analyzing images of parts, describing defects in plain language, suggesting fixes and even communicating with machines to carry out automatic adjustments.

MaVila was trained using a specialized approach that required

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A fully supported PhD position on Machine Learning for Scientific Computing is available in the Department of Mechanical and Aerospace Engineering at Rutgers University. The successful applicant will work under the supervision of Dr. George Moutsanidis and the desired start date is January 2026.

The PhD project will focus on the development and application of advanced machine learning techniques on computational fluid dynamics and computational solid mechanics. This includes both data-driven and physics-informed approaches, with potential applications in optimization, surrogate modeling, and uncertainty quantification.

Required Qualifications:

MS degree in Engineering, Applied Mathematics, or related field Solid academic background in the theory and application of novel machine learning techniques, such as, deep operator networks, graph neural networks, and generative AI methods. Solid academic background on scientific computing Excellent coding skills Exceptional analytical and problem-solving skills Strong communication and teamwork capabilities

Why join Rutgers

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