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11 people from 10 states sick with the same strain of Salmonella linked to frozen sprouted beans
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RSS feed source: Centers for Disease Control and Prevention--Office of Public Health Preparedness and Response
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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RSS feed source: Centers for Disease Control and Prevention--Office of Public Health Preparedness and Response
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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Dengue is a year-round risk in many parts of the world, with outbreaks commonly occurring every 2-5 years. Travelers to risk areas should prevent mosquito bites. Country List : Colombia, Ecuador, including the Galápagos Islands, Guatemala, Panama, Sudan, French Polynesia, including the island groups of Society Islands (Tahiti, Moorea, and Bora-Bora), Marquesas Islands (Hiva Oa and Ua Huka), and Austral Islands (Tubuai and Rurutu), Philippines, Fiji, Comoros, Tonga, Samoa, Cook Islands (New Zealand), Kiribati (formerly Gilbert Islands), includes Tarawa, Tabuaeran (Fanning Island), and Banaba (Ocean Island), Bangladesh, Mali, Tuvalu
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