Annonce en lien avec l’Action/le Réseau : Doctorants
Laboratoire/Entreprise : ANITI – Institut 3iA porté par l’université de Toulouse
Durée : PhD: 3 years – PostDoc: up to 4 years
Contact : mohamed.kaaniche@laas.fr
Date limite de publication : 2020-07-31
Contexte :
The University of Toulouse seeks to hire outstanding PhD and Post Doc candidates in artificial intelligence and in particular in machine learning at its new Institute on Artificial and Natural Intelligence (ANITI).
The French government, in consultation with an international jury of experts, has selected ANITI to be one of four, highly visible interdisciplinary institutes spearheading AI research, education, and economic development in France. ANITI has targeted as strategic areas mobility and transportation, and robotics/cobotics for the industry of the future.
More specifically, ANITI will combine fundamental research on the foundations of machine learning and on integrating data driven and reasoning based systems towards the following goals.
– Acceptability, Fair representative data for AI
– Certifiable AI toward autonomous critical Systems
– Assistants for design, decision, and Industrial processes
Starting operations this autumn, ANITI will bring together more than 200 researchers from universities, engineering schools, scientific and technological research organizations, and about thirty companies. More than thirty research chairs will be funded in this context, of which at least ten will be chaired by researchers brought in from the exterior. The project will also promote international mobility and collaboration to attract outstanding students and researchers.
Eventually, we will fund upwards of 40 PhD and 30 Post Doc positions. Successful candidates will have a unique opportunity of contributing to the ambitious research agenda of ANITI, and will be given excellent conditions for the development of their research skills, in terms of working conditions and laboratory facilities. The working language at the institute is English, and salaries are internationally competitive.
PhD Positions: duration 36 months, net salary: 2096€ per month with some teaching (64 hours per year on average)
Post Doc Positions : from one to four years. Net salary: negotiable with a minimum of 2600€ per month with some teaching (64 hours per year on average).
Relevant pointers about ANITI are available at: http://aniti.univ-toulouse.fr
The list of Chairs offering open PhD and Post-Doc positions with their contact information is available at: https://www.univ-toulouse.fr/ANITI-CHAIRS-EN
For more information on ANITI integrated projects, see https://www.univ-toulouse.fr/ANITI-IPs-EN
Application procedure :
Formal applications should include detailed CV, a motivation letter and transcripts of bachelors’ degrees. Samples of published research by the candidate and reference letters will be a plus.
Applications should be sent by email to:
aniti-phd@univ-toulouse.fr (for PhD applications)
aniti-postdoc@univ-toulouse.fr (for Post-Doc applications)
Sujet :
ANITI will combine fundamental research on the foundations of machine learning and on integrating data driven and reasoning based systems towards the following goals.
– Acceptability, Fair representative data for AI
– Certifiable AI toward autonomous critical Systems
– Assistants for design, decision, and Industrial processes
Starting operations this autumn, ANITI will bring together more than 200 researchers from universities, engineering schools, scientific and technological research organizations, and about thirty companies. Currently 24 research chairs are funded in this context. The project will also promote international mobility and collaboration to attract outstanding students and researchers.
Eventually, we will fund upwards of 40 PhD and 30 Post Doc positions. Successful candidates will have a unique opportunity of contributing to the ambitious research agenda of ANITI, and will be given excellent conditions for the development of their research skills, in terms of working conditions and laboratory facilities. The working language at the institute is English, and salaries are internationally competitive.
List of chairs:
IPA: Acceptability, Fair representative data for AI
———————-
J.F. Bonnefon. A detailed quantitative understanding of social expectations in two domains of moral AI: self-driving cars and algorithmic justice: address problems and ethical dilemmas that involve tradeoffs relevant to the design of autonomous vehicles inter alia.
C. Castets-Renard, Law, Accountability and Social Trust in AI: investigate a legal framework for making AI programs properly accountable. Legal issues like consumer protection, liability, and insurance need work before AI can gain full social trust
C. Hidalgo. Developing AI to Improve Global Governance: Advance the development of big data and AI tools to serve the general public and promote data driven decision making and AI ethics (public data enhanced with computer vision and NLP, digital twins).
B. Jullien AI and Competition. This research aims at fostering our theoretical and empirical understanding of the economics of information services using AI, with a special emphasis on the impact of AI on competition
J. M. Loubes, Fair & Robust Methods in Machine Learning: Analyse fair learning and bias using tools from statistics and optimal transport theory and contribute to explaining ML program behavior, anomaly detection and making ML methods more robust.
N. Dobigeon, Data-driven approximate Bayesian computation for fusion-based inference from heterogeneous remote sensing data: have applications to multi source multi-scale and multi temporal data
F. Gamboa, AI for physical models with geometric tools: will look at computer simulations for physical, chemical or biological phenomena, and seek to improve their analysis with application to various data driven deep learning models
IPB- Certifiable AI toward autonomous critical Systems
——————-
J. M. Loubes, Fair & Robust Methods in Machine Learning: Analyse fair learning and bias using tools from statistics and optimal transport theory and contribute to explaining ML program behavior, anomaly detection and making ML methods more robust.
S. Gratton. Efficient algorithms and Data Assimilation for computationally efficient constrained advanced learning, will design gradient based embeddable algorithms, that are provably convergent to 2nd order stationary points, and with a provable low complexity.
J. Marques-Silva. Deep Learner Explanation & Verification, will use the remarkable progress made by automated reasoners based on SAT, SMT, CP, ILP solvers (among others) to further explainable and robust data driven AI (Hybrid AI for proving robustness for neural networks).
J.B. Lasserre. Polynomial optimization for ML using sum of squares/Lasserre hierarchies and functions for data analysis will study approximation methods for non convex search spaces.
J. Bolte. Large scale optimization for AI, will study convergence properties/rates, global optimization and error bounds, design/optimization of underlying geometrical structure, optimization of adversarial models.
M. Teboulle. Pushing the frontier of nonconvex optimization to more general settings and understanding why it works, will classify the quality of local minima arising in highly nonconvex optimization problems with numerous local minima points, such as in neural networks, applying ideas from spin glasses or the protein folding problem to ML.
C. Pagetti. New certification approaches of critical AI based systems, e.g., integrating the notion of algorithm failure, non deterministic and unpredictable behavior.
J. Renault. Game Theory, Convergence for Generalized Adversarial Nets and other ML architectures: formal study of generalized adversarial networks and interactions of autonomous AI systems using stochastic.
D. Delahaye. AI for Air Traffic Management and Large Scale Urban Mobility: automation in air traffic management and UAV large scale trajectory planning.
IPC
—
T. Serre. Reverse-engineering the brain to build machines that can see and interpret the visual world as well as humans do: develop ML algorithms that can process visual data in ways that are closer to what humans do. Such systems will be robust and reliable though perhaps lacking performance of pure ML systems on certain tasks.
R. van Rullen. Deep learning with semantic, cognitive and biological constraints: brings experts from several disciplines in a multi-pronged approach to cognitive/bio-inspired models. It will study multimodal interactions in human brains as a source for more robust, less data demanding ML. AI algorithms from distributed intelligence will also be developed
F. Dehais. Neuro-adaptive Technology based Mixed-initiative to enhance Man-Machine Teams: study flexible mixed-initiative planning and execution paradigm involving humans interacting with artificial agents
R. Alami. Human Robot Interactions for cobot-industry applications, and highly adaptable service robots: integrate AI with a robotics for cognitive and interactive robot partners: autonomous teammate robots working with humans, cognitive and interactive assistants for frail people, highly adaptive service robots.
N. Mansard: Motion Generation for Complex Robots using Model-Based Optimization and Motion Learning: develop a unified yet tractable approach to motion generation for complex robots with arms and legs.
L. Travé-Massuyes. Synergistic transformations in model based and data based diagnosis: synergistically analyze transformations from model based diagnosis to exhibit fault indicators and data transformations from data based diagnosis methods
H. Fargier. Techniques for reducing complexity of algorithms for solving problems with uncertainty and preferences: investigate methods for compiling computations needed to solve combinatorial decision problems with preferences and uncertainty (typically above NP) transforming them into a simpler approximation
T. Schiex. AI for Computational Protein Design, will combine logical methods, automated reasoning and numerical methods to design proteins automatically, extending currently available automated reasoning technology to target problems beyond the NP-level
Profil du candidat :
Different profiles are relevant including:
-Artificial intelligence, machine learning, optimization, statistics, robotics, NLP, computer vision, Human-robot interaction, anomaly detection, etc.
Formation et compétences requises :
Masters/Engineer Diploma in the following areas
– Mathematics, Artificial intelligence, machine learning, optimization, statistics, robotics, NLP, computer vision, Human-robot interaction, anomaly detection, etc.
Adresse d’emploi :
Toulouse