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BigDataFR vous souhaite une excellente journée et vous propose de vous plonger dans "Le Monde des Mathématiques" !

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[Le Monde des Mathématiques] par Pierre-Michel Menger, Professeur au Collège de France et Pierre Verschueren, Maître de conférences à l'université de Franche-Comté

►Avant-Propos	►Présentation
✔️ Analyse l’environnement social dans lequel évoluent les chercheurs en mathématiques ✔️ Le plus abstrait, le plus élitiste et le plus international des mondes savants ✔️Un univers d’échanges constants et de compétition acharnée ►Sommaire ►Présentation ►À l'origine de ce livre : un constat !La France fournit à la fois : ✔️des mathématiciens récompensés par les plus hautes distinctions internationales ✔️et les élèves parmi les moins performants en calcul du monde	✔️Cet ouvrage collectif entend explorer les rouages de cette science prestigieuse, exigeante et intimidante que sont les mathématiques : ►Comment les mathématiciens travaillent-ils et sont-ils formés ? ►Comment leurs idées rivalisent-elles dans l’espace mondial ? ►Quelle est la valeur du génie et de la communauté scientifique dans l’ethos de la profession ? ►Pourquoi les femmes y sont-elles encore très minoritaires ? ►Quels ont été les effets de la réforme des « mathématiques modernes » ?
Autant de questions qui permettent d’aborder les spécificités du « monde des mathématiques », structuré – comme l’art ou le sport – par la visée à l'universalité de son langage, la rigueur de son exercice et son incessante créativité.
Auteurs
Sous la direction de	Avec les contributions de
✔️ Pierre-Michel Menger, sociologue, Professeur au Collège de France et ✔️ Pierre Verschueren, historien, Maître de conférences à l'université de Franche-Comté	✔️ Michael J. Barany, Odile Chatirichvili, Rémy Cerda, Karine Chemla, Sophie Coeuré, Simon Decaens, Renaud d’Enfert, Samson Duran, Hélène Gispert, Colin Marchika, Julien Muller, Philippe Nabonnand, Alice Pavie, Jeanne Peiffer, Laurent Rollet, Martina Schiavon, Jean-Marc Schlenker, Martine Sonnet, Yannick Vincent, Bernard Zarca
►Sommaire ►Présentation ►Plus d'informations

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►Experience/Bonus Point for	►PASQAL offers
✔️ Solid knowledge of either Classical or Quantum Graph Machine Learning ✔️ Experience working with Python (Knowledge of Julia, C++, CUDA is a bonus) ✔️ Experience with Neural Network quantum states ✔️ Experience working with experimental groups ✔️ Experience in cold atom quantum simulators ✔️ Experience with large-scale numerical simulations	✔️ Beautiful offices in Paris, London or Amsterdam ✔️ A flexible rhythm of face-to-face work with 2-3 remote days a week ✔️ A dynamic and close-knit international team ✔️ A key role in a growing start-up ✔️ Free time to train and go to conferences/meetups
Apply Now/More information - W/M ✔️Principal Scientist-Quantum Machine Learning - Amsterdam, Netherlands ✔️Lead Quantum Machine Learning Developer - Amsterdam, Netherlands ✔️Quantum Algorithm Developer (Mid-Senior level) - Netherlands/France

►PASQAL Raises €100 Million Series B Funding to Advance Neutral Atoms Quantum Computing

By Georges-Olivier REYMOND CEO

►Read Press Release - Career - More information

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[Still available] Machine Learning and Data Sciences for Financial Markets, Cambridge University Press (June 23)

✔️A Guide to Contemporary Practices

►Machine Learning and Data Sciences for Financial Markets	►The text is structured around three main areas
✔️ Leveraging the research efforts of more than 60 experts in the area, this book reviews cutting-edge practices in machine learning for financial markets. ✔️ Instead of seeing machine learning as a new field, the authors explore the connection between knowledge developed in quantitative finance over the past 40 years and modern techniques generated by the current revolution in data sciences and AI.	✔️ 1-Interacting with investors and asset owners Covers robo-advisors and price formation ✔️ 2-Towards better risk intermediation Discusses derivative hedging, portfolio construction, and machine learning for dynamic optimization ✔️ 3-Connections with the real economy Explores nowcasting, alternative data, and ethics of algorithms.
✔️ Accessible to a wide audience This invaluable resource will allow practitioners to include machine learning driven techniques in their day-to-day quantitative practices. While students will build intuition and come to appreciate the technical tools and motivation behind the theory.
Editors
►Agostino Capponi, Columbia University Associate Professor in the Department of Industrial Engineering and Operations Research at Columbia University.	►Charles-Albert Lehalle Abu Dhabi Investment Authority (ADIA) Global Head of Quantitative R&D at Abu Dhabi Investment Authority
✔️ He conducts research in financial technology and market microstructure. ✔️ His work has been recognized with the National Science Foundation (NSF) CAREER Award and a J.P. Morgan AI Research award. ✔️ Capponi is a co-editor of Management Science and Mathematics and Financial Economics. ✔️ He is a Council member of the BACHELIER FINANCE SOCIETY and recently served as Chair of the SIAM-FME and INFORMS Finance.	✔️ Visiting Professor at Imperial College London. ✔️He has a Ph.D. in machine learning, was previously Head of Data Analytics at Capital Fund Management (CFM), and held different Global Head positions at Crédit Agricole CIB. ✔️Recognized as an expert in market microstructure, Lehalle is often invited to present to regulators and policy-makers.
►Table of Contents ►Excerpt ►Index

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