Thrilled to share that MathWorks has secured the #4 spot on Glassdoor's Best Places to Work 2024 list! 🎉 Big shoutout to our exceptional team for making it happen! Read about it here 👉 http://spr.ly/6040RWtNG #GlassdoorBPTW #MathWorks
MathWorks
Software-Entwicklung
Natick, MA 409,558 followers
Accelerating the Pace of Engineering and Science
Über uns
MathWorks is the leading developer of mathematical computing software. Engineers and scientists worldwide rely on its products to accelerate the pace of discovery, innovation, and development. MATLAB, the language of technical computing, is a programming environment for algorithm development, data analysis, visualization, and numeric computation. Simulink is a graphical environment for simulation and Model-Based Design for multidomain dynamic and embedded systems. MATLAB and Simulink are also fundamental teaching and research tools in the world's universities and learning institutions. Founded in 1984, MathWorks employs more than 6000 people in 16 countries, with headquarters in Natick, Massachusetts, USA.
- Website
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http://mathworks.com
External link for MathWorks
- Industrie
- Software-Entwicklung
- Größe des Unternehmens
- 5,001-10,000 employees
- Hauptsitz
- Natick, MA
- Typ
- In Privatbesitz
- Gegründet
- 1984
Standorte
Employees at MathWorks
Aktualisierungen
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At KTH University, researchers are harnessing the power of #ModelBasedDesign with MATLAB and Simulink to simulate, optimize, and implement control algorithms. This technology enables automated underwater vehicles (AUVs) to tackle longer and more complex missions! 🤿
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🍟 🍣 Predicting fries and sushi! Deep learning techniques, particularly CNNs, are widely used for object recognition tasks. CNNs learn the distinguishing features of objects by analyzing large datasets, making it possible to identify items like french fries or sushi. There are two main approaches for object recognition using deep learning: 👉 Training a model from scratch: 📊 This involves creating a large labeled dataset and designing a network architecture to learn and identify features. Although effective, it requires significant data and setup. 👉 Using a pretrained model: 🔄 This approach involves transfer learning, where an existing model like AlexNet or GoogLeNet is fine-tuned with new data. It is less time-intensive and leverages pre-existing training. Deep learning; provides high accuracy but depends on large datasets for precise predictions. 📈
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See how undergraduate and graduate engineering students learn the many facets of software-defined radio by competing with other university teams to build practical RF systems that fulfill a real-world objective. Using USRP radios from NI, they learn about RF concepts, embedded programming, algorithm development, and modern communications systems like 5G ➡️ https://spr.ly/6044lNYuu
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MathWorks Natick interns dedicated an afternoon to packing kits for the Discovery Museum's Traveling Science Workshop this summer! We're proud to support this fantastic program that sparks interest in STEM through engaging, hands-on workshops delivered directly to classrooms. Thanks to their hard work, over 120 kits are ready to inspire young minds for the upcoming school year! A big shoutout to Discovery Museum for partnering with us. 🚀📚
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Our hiring process is designed to make sure you get the chance to learn about MathWorks while our hiring team learns about you. Learn about the hiring process for full-time and internship positions in the Engineering Development Group at MathWorks ➡️ https://spr.ly/6042cqjve
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Detecting Kelp Forests through Deep Learning!! Are you interested in the convergence of AI and environmental science? A team of students used deep learning to map kelp forests using satellite imagery for the Kelp Wanted challenge 👉 https://spr.ly/6048lyysq
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