🔔 Attention, date limite en vue! 👉 Bourses d’études supérieures du Canada au niveau du doctorat (BESC D) 👉 Bourses d’études supérieures du CRSNG – doctorat (ES D) 👉 Bourses postdoctorales (BP) 🗓️ 17 octobre 2024, avant 20 h (HE) Instructions ▶️ tinyurl.com/ydba2wju #SoutienCRSNG #CRSNG_BESCD #CRSNG_ESD #CRSNG_BP
Natural Sciences and Engineering Research Council of Canada (NSERC)
Government Administration
Ottawa, Ontario 42,496 followers
NSERC (Government of Canada) / CRSNG (Gouvernement du Canada)
About us
NSERC invests over $1 billion each year in natural sciences and engineering research in Canada. Our investments deliver discoveries, valuable world-firsts in knowledge claimed by a brain trust of over 11,000 professors, world-leading researchers in their fields. Our investments also enable partnerships and collaborations that connect industry with discoveries and the people behind them. Researcher-industry partnerships established by NSERC help inform R&D, solve scale-up challenges and reduce the risks of developing high-potential technology. Our investments also provide scholarships and hands-on training experience for the next generation of science and engineering leaders in Canada, more than 30,000 post-secondary students and post-doctoral fellows. Terms of Service: http://bit.ly/2b5VBdO
- Website
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http://www.nserc-crsng.gc.ca
External link for Natural Sciences and Engineering Research Council of Canada (NSERC)
- Industry
- Government Administration
- Company size
- 201-500 employees
- Headquarters
- Ottawa, Ontario
- Type
- Government Agency
- Founded
- 1978
Locations
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Primary
125 Zaida Eddy Private
2nd floor
Ottawa, Ontario K1R 0E3, CA
Employees at Natural Sciences and Engineering Research Council of Canada (NSERC)
Updates
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🛎️ Application deadline alert 👉 Canada Graduate Scholarships - Doctoral (CGS D) program 👉 Postgraduate Scholarships - Doctoral (PGS D) program 👉 Postdoctoral Fellowships (PDF) program 🗓️ October 17, 2024 before 8 pm (ET) Instructions ▶️ tinyurl.com/2m3pdjzj #NSERCsupport #NSERC_CGSD #NSERC_PGSD #NSERC_PDF
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📢 Are you passionate about promoting the participation and retention of underrepresented groups in #STEM? Learn more about the Chairs for Inclusion in Science and Engineering program in the province of Quebec. #NSERC_CISE ▶️ tinyurl.com/2xy5rb9t
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📢 Vous avez à cœur d’encourager les membres des groupes sous-représentés à étudier et à faire carrière en #STIM ? Renseignez-vous sur le Programme de chaires pour l’inclusion en sciences et en génie au Québec. ▶️ tinyurl.com/mv2buzvd #CRSNG_CISG
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👏 #NSERC congratulates University of Toronto Professor Emeritus Geoffrey Hinton on being named a winner of the #NobelPrize in Physics. We are proud to have provided continuous #NSERCsupport for the earliest pioneering discoveries of one of the world’s “Godfathers of #AI”.
BREAKING NEWS The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Physics to John J. Hopfield and Geoffrey E. Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks.” This year’s two Nobel Prize laureates in physics have used tools from physics to develop methods that are the foundation of today’s powerful machine learning. John Hopfield created an associative memory that can store and reconstruct images and other types of patterns in data. Geoffrey Hinton invented a method that can autonomously find properties in data, and so perform tasks such as identifying specific elements in pictures. When we talk about artificial intelligence, we often mean machine learning using artificial neural networks. This technology was originally inspired by the structure of the brain. In an artificial neural network, the brain’s neurons are represented by nodes that have different values. These nodes influence each other through connections that can be likened to synapses and which can be made stronger or weaker. The network is trained, for example by developing stronger connections between nodes with simultaneously high values. This year’s laureates have conducted important work with artificial neural networks from the 1980s onward. John Hopfield invented a network that uses a method for saving and recreating patterns. We can imagine the nodes as pixels. The Hopfield network utilises physics that describes a material’s characteristics due to its atomic spin – a property that makes each atom a tiny magnet. The network as a whole is described in a manner equivalent to the energy in the spin system found in physics, and is trained by finding values for the connections between the nodes so that the saved images have low energy. When the Hopfield network is fed a distorted or incomplete image, it methodically works through the nodes and updates their values so the network’s energy falls. The network thus works stepwise to find the saved image that is most like the imperfect one it was fed with. Geoffrey Hinton used the Hopfield network as the foundation for a new network that uses a different method: the Boltzmann machine. This can learn to recognise characteristic elements in a given type of data. Hinton used tools from statistical physics, the science of systems built from many similar components. The machine is trained by feeding it examples that are very likely to arise when the machine is run. The Boltzmann machine can be used to classify images or create new examples of the type of pattern on which it was trained. Hinton has built upon this work, helping initiate the current explosive development of machine learning. Learn more Press release: https://bit.ly/4gCTwm9 Popular information: https://bit.ly/3Bnhr9d Advanced information: https://bit.ly/3TKk1MM
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👏 Le #CRSNG félicite Geoffrey Hinton, professeur émérite à la University of Toronto, d’avoir reçu le #PrixNobel de physique. Nous sommes fiers du #soutienCRSNG continu pour les premières découvertes pionnières d’un des « parrains de l’#IA ».
BREAKING NEWS The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Physics to John J. Hopfield and Geoffrey E. Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks.” This year’s two Nobel Prize laureates in physics have used tools from physics to develop methods that are the foundation of today’s powerful machine learning. John Hopfield created an associative memory that can store and reconstruct images and other types of patterns in data. Geoffrey Hinton invented a method that can autonomously find properties in data, and so perform tasks such as identifying specific elements in pictures. When we talk about artificial intelligence, we often mean machine learning using artificial neural networks. This technology was originally inspired by the structure of the brain. In an artificial neural network, the brain’s neurons are represented by nodes that have different values. These nodes influence each other through connections that can be likened to synapses and which can be made stronger or weaker. The network is trained, for example by developing stronger connections between nodes with simultaneously high values. This year’s laureates have conducted important work with artificial neural networks from the 1980s onward. John Hopfield invented a network that uses a method for saving and recreating patterns. We can imagine the nodes as pixels. The Hopfield network utilises physics that describes a material’s characteristics due to its atomic spin – a property that makes each atom a tiny magnet. The network as a whole is described in a manner equivalent to the energy in the spin system found in physics, and is trained by finding values for the connections between the nodes so that the saved images have low energy. When the Hopfield network is fed a distorted or incomplete image, it methodically works through the nodes and updates their values so the network’s energy falls. The network thus works stepwise to find the saved image that is most like the imperfect one it was fed with. Geoffrey Hinton used the Hopfield network as the foundation for a new network that uses a different method: the Boltzmann machine. This can learn to recognise characteristic elements in a given type of data. Hinton used tools from statistical physics, the science of systems built from many similar components. The machine is trained by feeding it examples that are very likely to arise when the machine is run. The Boltzmann machine can be used to classify images or create new examples of the type of pattern on which it was trained. Hinton has built upon this work, helping initiate the current explosive development of machine learning. Learn more Press release: https://bit.ly/4gCTwm9 Popular information: https://bit.ly/3Bnhr9d Advanced information: https://bit.ly/3TKk1MM
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Natural Sciences and Engineering Research Council of Canada (NSERC) reposted this
C’est un honneur de participer à un événement si important et toujours plus grandiose d’année en année. Le 2 novembre prochain, assistez à notre conférence ou passez nous voir à notre kiosque!
Nous aurons le privilège d’assister à une conférence de fermeture positive qui suscitera des réflexions profondes sur l’équité, la diversité et l’inclusion. Voici la description de la conférence de fermeture "Agir pour faire la différence", présentée par la Chaire pour les femmes en sciences et en génie au Québec ,plus précisément par Eve Langelier et Joëlle Pelletier-Nolet! Ne manquez pas cette opportunité d'assister au congrès IngénieurE au Féminin et réservez dès maintenant votre place avec le lien suivant: https://lnkd.in/eKB-bYWz Psst les places aux activités sont limitées alors fait vite ! Vous avez jusqu’au 15 octobre 2024 pour vous inscrire ! 📅 Date : 1 et 2 novembre 2024 📍 Lieu : Centre de Foires Sherbrooke Pour plus de détails sur les conférences et ateliers, consulter le site internet de Génie au Féminin : https://lnkd.in/eG75NABi
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📢 The Global Research Council (GRC) is hosting its annual Americas Virtual Seminars in the coming weeks. This year’s sessions, designed to align with the theme of the 2025 GRC Annual Meeting, will focus on two pivotal subjects: #ResearchSecurity and Integrity and Guidance and Adaptation of #AI in Science, Technology and Innovation (STI). On October 10, #NSERC's Director of Research Security, Shawn McGuirk, will be a panelist at a session exploring the landscape of research integrity and security in the Americas. Two weeks later, on October 24, #NSERC's Chief Data Officer, Michael Lam, will participate in a session on AI applications for STI funding agencies and other related actors. Register ▶️ tinyurl.com/mw74p7a2
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📣 Au cours des prochaines semaines, le Global Research Council (GRC) tiendra l’édition annuelle des Americas Virtual Seminars (en anglais). Les séances de cette année, conçues pour cadrer avec le thème de l’assemblée annuelle du GRC de 2025, porteront sur deux sujets cruciaux, soit la sécurité et l’intégrité de la recherche, d’une part, et l’encadrement et l’adaptation de l’IA dans les domaines des sciences, de la technologie et de l’innovation (STI), d’autre part. Le 10 octobre prochain, Shawn McGuirk, directeur de la sécurité de la recherche au #CRSNG, sera l’un des panélistes invités pour parler de l’intégrité et de la #SécuritéDeLaRecherche dans la région des Amériques. Deux semaines plus tard, le 24 octobre, Michael Lam, dirigeant principal des données au #CRSNG, participera à une séance sur les applications de l’#IA pour les organismes de financement des STI et d’autres parties prenantes concernées. Inscription ▶️ tinyurl.com/mw74p7a2
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REMINDER 📢 The deadline to apply for the Research Tools and Instruments program is fast approaching! Deadline 🗓️ October 25, 2024 at 8:00 pm (ET) Instructions ▶️ tinyurl.com/34huvvv2 #NSERCsupport #NSERC_RTI