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--- internships [2019/02/07 10:35]
cani [Master 1 internships (4 to 5 months)]
+++ internships [2020/04/28 18:59]
payan
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 The report must be at least 30 pages long. It must contain a general presentation and motivation for the topic, point-out the related  challenges, and describe state-of-the-art methods in this field. The contributions of the student must be clearly highlighted and explained in details. There is no need for an exhaustive description of all codes produced during the internship. However, algorithms highlighting the challenging tasks solved by the student must be presented and explained in the report (if needed, relevant parts of the code can be included as well in an appendix).
+=============== Subjects for innterships 2019-2020  (4 to 5 months) ======================
+** Strollhunt Limited (United Kingdom) - COMPUTER VISION ENGINEER INTERN (REMOTE) ** - 30 March 2020
+Link internship offer : http://www.lix.polytechnique.fr/Labo/Marie-Paule.Cani/MasterAI/lib/exe/fetch.php?media=2020_internship_offer_computer_vision_engineer_intern_remote_.pdf
+Company Name: Strollhunt Limited
+Company address: 47B Aberdeen Road, London N5 2XD, United Kingdom
+Contact : Lucas Braunschvig
+** YNSECT - Paris **
+Link internship offer : http://www.lix.polytechnique.fr/Labo/Marie-Paule.Cani/MasterAI/lib/exe/fetch.php?media=stage_ynsect_ml-ynsect.pdf
+** Inarix - Paris **
+Computer Vision startup
+link offer : http://www.lix.polytechnique.fr/Labo/Marie-Paule.Cani/MasterAI/lib/exe/fetch.php?media=2020_intership_machine_learning_intern-1_inarix.pdf
+Contact: M.Alexandre Hannebelle, Head Of Data Scient
+Email:alexandre@inarix.com
+** Université de Toulouse **
+** Counterfactual models and geodetic deformations **
+The internship will take place at [IMT or ANITI/B612] under the direction of Jean-Michel Loubes and Laurent Risser.
+For more information: http://www.lix.polytechnique.fr/Labo/Marie-Paule.Cani/MasterAI/doku.php?id=internships&image=2019_2020_stagexrisserloubes-2-modeles_contrefactuels_et_deformations_geodesiques.pdf&ns=&tab_details=view&do=media&tab_files=files
+Contacts :
+Loubes <loubes@math.univ-toulouse.fr>
+	laurent risser <laurent.risser@math.univ-toulouse.fr>
+** Learning Algorithms Systems Laboratory (LASA) - EPFL, Switzerland **
+** Fusing visual and tactile sensors information to infer object’s geometry **
+http://www.lix.polytechnique.fr/Labo/Marie-Paule.Cani/MasterAI/lib/exe/fetch.php?media=master_thesis_proposal_2020_fusing_visual_and_tactile_sensors_information_to_infer_object_s_geometry_.pdf
+contact : Saurav Aryan, Doctoral candidate (Aryan Saurav <saurav.aryan@epfl.ch>)
+http://lasa.epfl.ch
+** INRIA **
+** Life Long Deep Learning (LLDL) Challenge Preparation  **
+Abstract
+[[Url |text]].
+Topic: Machine Learning, Deep Learning City and country: UPSud/INRIA Paris-Saclay (Gif-sur-Yvette) and Google (Zurich).  Team or project in the lab: Isabelle Guyon (UPSud/INRIA), Michèle Sebag (CNRS),  Zhengying Liu (PhD. Student). International collaboration with André Elisseeff (Google Zurich), Sergio Escalera (Univ. Barcelona), and Wei-Wei Tu (4Paradigm, China). Name and mail of the advisors: Isabelle Guyon (iguyon@lri.fr), Michèle Sebag (sebag@lri.fr), Zhengying Liu (zhengying.liu@lri.fr). Name and mail of the head of the department: Yannis Manoussakis (yannis.manoussakis@lri.fr) LRI, UPSud Paris-Saclay.
+General presentation of the topic: Machine Learning problems (including Deep Learning) are often thought of as a “onetime” effort in which models are trained and tested on data coming from a common invariant source. Yet, in practice, data sources may drift and/or variants of an original problem may recur. Thus, it is important to capitalize on previously seen data and/or situations to adapt or solve new problems. Going beyond the usual setting of Transfer Learning or Domain Adaptation, researchers have been starting to investigate the possibility of Life-Long Machine Learning. In parallel, Deep Learning methods have known in the recent year tremendous success in applications, but also generated a lot of
+frustrations from users eager to try them out, but slowed down by the effort needed to tune hyper-parameters by trial and errors.  To address the latter problem, we have engaged in a series of data science challenges with Google Zurich and 4Paradigm to foster research in the area of self-tuning machine learning algorithm (http://autodl.chalearn.org), which are part of the official selection of the prestigious NeurIPS conference. To prepare these challenges, we have formatted nearly 100 datasets coming from a variety of domains and including speech, images, videos, text, and tabular data, all formatted in a unified way, but NOT preprocessed into fixed-length feature vectors. Therefore, we have a wealth of data making it possible to organize an unprecedented LLDL challenge.
+Objective of the internship:
+The aim of this internship will be to help prepare the LLDL challenge. The intern will produce baseline results for the challenge using state-of-the-art techniques to bootstrap participation in the competition. Available to him will be the solutions of the winners to past AutoDL challenges. However, such solutions do not involve “meta-learning”, i.e. learning from exposure to past datasets to perform better on new tasks. The intern with explore with meta-learning strategies, including reinforcement learning. The intern will join a dynamic and motivated team of challenge organizers and may contribute to a publication on the challenge design.
+ Bibliographic references: [1] I. Guyon, K. Bennett, G. Cawley, H. J. Escalante, S. Escalera, T. K. Ho, B. Ray, M. Saeed, A. Statnikov, and E. Viegas. AutoML challenge 2015: Design and first results.
+ [2] Liu Z, Bousquet O, Elisseeff A, Escalera S, Guyon I, Jacques J, Pavao A, Silver D, Sun-Hosoya L, Treguer S, Tu WW. AutoDL Challenge Design and Beta Tests-Towards automatic deep learning. InCiML workshop@ NIPS2018 2018 Dec.
+ [3] B. Zoph, and L. Quoc. "Neural architecture search with reinforcement learning." arXiv preprint arXiv:1611.01578(2016).
+ [4] Elsken, Thomas, Jan Hendrik Metzen, and Frank Hutter. "Neural architecture search: A survey." arXiv preprint arXiv:1808.05377 (2018).
+ [5] Zhengying Liu, Isabelle Guyon, Julio Jacques,Jr, Meysam Madadi, Sergio Escalera, Adrien Pavao, Hugo Jair Escalante, Wei-Wei Tu, Zhen Xu, Sebastien Treguer, AutoCV Challlenge Design and Baseline Results. https://hal.archives-ouvertes.fr/hal-02265053
+ [6] Madrid, J.G., Escalante, H.J., Morales, E.F., Tu, W.W., Yu, Y., Sun-Hosoya, L., Guyon, I. and Sebag, M., 2019. Towards AutoML in the presence of Drift: first results. arXiv preprint arXiv:1907.10772.
+Expected ability of the candidate:  We are accepting candidates with background in machine learning, statistics, scientific modeling, signal processing and control (or at least a subset if those). The candidate should have the ability of working on cross-disciplinary problems, have a strong math background, and the experience or strong desire to work on practical problems. Good
+programming skills are also required. Experience with TensorFlow and/or PyTorch is preferred. Experience in GPU/TPU computing is a plus.
+** INRIA **
+** Automatic Deep Learning (AutoDL) Benchmark with extensive GPU usage **
+ Topic: Machine Learning, Deep Learning City and country: UPSud/INRIA Paris-Saclay (Gif-sur-Yvette) and Google (Zurich).  Team or project in the lab: Isabelle Guyon (UPSud/INRIA), Michèle Sebag (CNRS),  Zhengying Liu (PhD. Student). International collaboration with André Elisseeff (Google Zurich) and Wei-Wei Tu (4Paradigm, China). Name and mail of the advisors: Isabelle Guyon (iguyon@lri.fr), Michèle Sebag (sebag@lri.fr), Zhengying Liu (zhengying.liu@lri.fr). Name and mail of the head of the department: Yannis Manoussakis (yannis.manoussakis@lri.fr) LRI, UPSud Paris-Saclay.
+ General presentation of the topic: Despite all the recent successes in Machine Learning in general and Deep Learning in particular, there is still a lot of room for improvement. Indeed, even though there has been significant advances both in hardware and software available for implementing and efficiently running large scale Machine Learning systems, a key bottleneck still remains: designing appropriate model architectures and tuning hyper-parameters is still a very tedious and labor-intensive process. Training a complex deep network is often a long and frustrating trial-and-error process involving lots of heuristics and requiring a fair amount of practical experience and expertise. Even worse, the difficulty of those tasks often translates into lack of reproducibility of the published results. To address this problem, we have engaged in a series of data science challenges with
+Google Zurich and 4Paradigm to foster research in the area of self-tuning machine learning algorithm (http://autodl.chalearn.org) which are part of the official selection of the prestigious NeurIPS conference. We also won an award to use the new supercomputer
+Jean Zay http://www.idris.fr/annonces/annonce-jean-zay-eng.html for the purpose of conducting intensive simulations of the problem of AutoDL.
+Objective of the internship: The aim of this internship will be to conduct systematic experiments to reproduce the results of the AutoDL competition and go beyond. One challenging aspect of the Google design is that the participants were exposed to data coming from a variety of domains and including speech, images, videos, text, and tabular data, all formatted in a unified way, but NOT preprocessed into fixed-length feature vectors. The winners have made their code publicly available and explained their methods in fact sheets, but clearly their methods involve a lot of ad hoc engineering. The purpose of this internship is then to identify the key ingredients of success of the methods and create a simplified , principled, and robust methodology. This internship may lead to a publication, in collaboration with the organizers.
+Bibliographic references: [1] I. Guyon, K. Bennett, G. Cawley, H. J. Escalante, S. Escalera, T. K. Ho, B. Ray, M. Saeed, A. Statnikov, and E. Viegas. AutoML challenge 2015: Design and first results.
+ [2] Liu Z, Bousquet O, Elisseeff A, Escalera S, Guyon I, Jacques J, Pavao A, Silver D, Sun-Hosoya L, Treguer S, Tu WW. AutoDL Challenge Design and Beta Tests-Towards automatic deep learning. InCiML workshop@ NIPS2018 2018 Dec.
+ [3] B. Zoph, and L. Quoc. "Neural architecture search with reinforcement learning." arXiv preprint arXiv:1611.01578(2016).
+ [4] Elsken, Thomas, Jan Hendrik Metzen, and Frank Hutter. "Neural architecture search: A survey." arXiv preprint arXiv:1808.05377 (2018).
+ [5] Zhengying Liu, Isabelle Guyon, Julio Jacques,Jr, Meysam Madadi, Sergio Escalera, Adrien Pavao, Hugo Jair Escalante, Wei-Wei Tu, Zhen Xu, Sebastien Treguer, AutoCV Challlenge Design and Baseline Results. https://hal.archives-ouvertes.fr/hal-02265053
+ [6] Ying, Chris, Aaron Klein, Esteban Real, Eric Christiansen, Kevin Murphy, and Frank Hutter. "Nas-bench-101: Towards reproducible neural architecture search." arXiv preprint arXiv:1902.09635 (2019).
+ Expected ability of the candidate:  We are accepting candidates with background in machine learning, statistics, scientific modeling, signal processing and control (or at least a subset if those). The candidate should have the ability of working on cross-disciplinary problems, have a strong math background, and the experience or strong desire to work on practical problems.
+Good programming skills are also required. Experience with TensorFlow and/or PyTorch is preferred. Experience in GPU/TPU computing is a plus.
+** LJK-Inria Grenoble  **
+** Research internships at LJK-Inria Grenoble, in Computer Graphics, contact Fabrice Neyret <Fabrice.Neyret@imag.fr> **
+Link Abstract :
+      M2R (+ X-M1): Playing with Fourier spectrum for real-time ocean explorer and texture generation
+http://www-evasion.imag.fr/Membres/Fabrice.Neyret/Etudiants/sujet1-2020.html
+      M2R (+ X-M1): Procedural modeling of 3D realistic galactic dust and nebulas
+http://www-evasion.imag.fr/Membres/Fabrice.Neyret/Etudiants/sujet2-2020.html
+      M2R (+ X-M1): Real-time walk-through the Milky-Way: continuum to points, on-the-fly generation, and mixing with catalog
+http://www-evasion.imag.fr/Membres/Fabrice.Neyret/Etudiants/sujet3-2020.html
+      M2R: Revisiting volumetric ray-tracing to make it well-posed
+http://www-evasion.imag.fr/Membres/Fabrice.Neyret/Etudiants/sujet4-2020.html
+** Ecole Polytechnique / LIX / STREAM - Master 2 level Artificial Intelligence and advanced Visual Computing **
+https://www.lix.polytechnique.fr/stream/job-offer/
+** TOTAL **
+** Topological Data Analysis - 6-month internship - Master 2 level or end of engineering school **
+Abstract
+[[Url |text]]. As part of a collaboration with Total, we are actively looking for a student with an interest in Topological Data Analysis for a 6-month internship at Master 2 level or at the end of an engineering school, with a view to continuing with a CIFRE thesis.
+Candidates already holding an M2 degree are invited to apply directly for the CIFRE thesis.
+The internship and thesis focus on the exploitation, using topological methods, of volumes of probability of the presence of seismic faults generated by deep learning.
+Desired start date: April 1, 2020.
+Location of the internship: Sorbonne University, Paris (metro Jussieu)
+The detailed subject is available below:
+https://julien-tierny.github.io/stuff/openPositions/internship2020_su_total.pdf
+** TOTAL **
+** Topological Data Analysis - 6-month internship - Master 2 level or end of engineering school with a continuation in thesis **
+Abstract
+We are actively looking for a student (or two) with an interest in Topological Data Analysis for a 6-month internship at Master 2 level or at the end of an engineering school, with a view to continuing with a thesis. The algorithms designed during the internship will be implemented in C++ within the open-source library "Topology ToolKit" (https://topology-tool-kit.github.io/) that we are developing with our collaborators.
+Desired start date: April 1, 2020.
+Location of the internship: Sorbonne University, Paris (metro Jussieu)
+ Detailed topics are available below:
+https://julien-tierny.github.io/stuff/openPositions/internship2020a.pdf
+https://julien-tierny.github.io/stuff/openPositions/internship2020b.pdf
+https://julien-tierny.github.io/stuff/openPositions/internship2020c.pdf
+If you already have a Master's degree (or equivalent) and are interested in a thesis related to these subjects, please send us your application letter with an updated CV.
+Dr. Julien Tierny
+CNRS Researcher
+Sorbonne University
+http://lip6.fr/Julien.Tierny
+** LJK-Inria Grenoble  **
+** Research internships at LJK-Inria Grenoble, in Computer Graphics, contact Fabrice Neyret <Fabrice.Neyret@imag.fr> **
+Link Abstract :
+      M2R (+ X-M1): Playing with Fourier spectrum for real-time ocean explorer and texture generation
+http://www-evasion.imag.fr/Membres/Fabrice.Neyret/Etudiants/sujet1-2020.html
+      M2R (+ X-M1): Procedural modeling of 3D realistic galactic dust and nebulas
+http://www-evasion.imag.fr/Membres/Fabrice.Neyret/Etudiants/sujet2-2020.html
+      M2R (+ X-M1): Real-time walk-through the Milky-Way: continuum to points, on-the-fly generation, and mixing with catalog
+http://www-evasion.imag.fr/Membres/Fabrice.Neyret/Etudiants/sujet3-2020.html
+      M2R: Revisiting volumetric ray-tracing to make it well-posed
+http://www-evasion.imag.fr/Membres/Fabrice.Neyret/Etudiants/sujet4-2020.html
+** Unity Technologies **
+Unity Technology est une entreprise internationale (+2500 employés) qui développe le moteur de jeux vidéo Unity.
+Unity a une antenne de recherche à Grenoble spécialisée dans le domaine de l'image de synthèse et du machine learning. Nous recherchons régulièrement des candidats pour des stages recherche ** niveau ingénieur/M2 ** avec possibilité de poursuite en thèse CIFRE. Nous définissons les sujets de stage en fonction des intérêts des candidats et de ceux de l'équipe.
+Plus d'informations sont disponibles sur le site de l'équipe de Grenoble.
+Personnel : http://unity-grenoble.github.io/website/people.html
+Liste des travaux et des publications : http://unity-grenoble.github.io/website/publications.html
+Offre de stage et d'emploi : http://unity-grenoble.github.io/website/news.html
+** ANATOSCOPE **
+Topic of the internship: Creation and analysis of a 3D format based on stacked 2D contours
+Level: Internship (M2)
+Duration: 6 months
+Location: Grenoble (Montbonnot)
+Framing: Ulysse Vimont
+Compensation: to be discussed
+Key Words : 3D, C++, mode lisation surfacique
+Contact : vimont@anatoscope.com, job@anatoscope.com
+link :http://www.lix.polytechnique.fr/Labo/Marie-Paule.Cani/MasterAI/doku.php?id=start&image=sujet_stage_anatoscope_spr_compressed.pdf&ns=&tab_details=view&do=media
+** IMAGEENS **
+** Internship topic: Automatic classification of cardiovascular medical images **
+www.imageens.com
+months internship, starting in March 2020
+link : http://www.lix.polytechnique.fr/Labo/Marie-Paule.Cani/MasterAI/doku.php?id=internships&image=sujetstage_classification-1_imageens.pdf&ns=&tab_details=view&do=media
+==== PhD  ====
+** 2020 **
+**Offre de poste PhD en Développement d’un simulateur en réalité virtuelle pour l’équipe nationale olympique de boxe, à l’École de technologie supérieure, Canada (Fall 2020 – septembre) **
+Le projet :
+-	consiste à développer un simulateur en réalité virtuelle pour l’équipe nationale olympique de boxe.
+-	comprendra des notions de vision par ordinateur, synthèse de mouvement humain et réalité virtuelle.
+Le poste est à :
+-	l’École de technologie supérieure, 2e plus grande école d’ingénieurs au Canada, sous ma supervision et en collaboration avec l’Institut National du Sport du Québec.
+Les détails sont disponibles ici :
+https://www.etsmtl.ca/recherche/etudes-superieures-et-recherche/projets-de-recherche-pour-etudiants/developpement-d%e2%80%99un-simulateur-en-realite-virtuelle/
+Contact :
+David Labbe, ing., Ph.D. | Professeur
+Département de génie logiciel et des TI
+École de technologie supérieure |  1100, rue Notre-Dame Ouest  |  Montréal (Qc) Canada  |  H3C 1K3
+Tél.: 514 396-8526   |  Bureau A-4455
+Chercheur
+Laboratoire d'imagerie et d'orthopédie
+Centre de recherche du CHUM
+Tél.: 514-890-8000 p31377 | Bureau R11.480
+** PhD position in Computer Science in RV/IA -  IRA2 group/ IBISC Lab, Paris Saclay University (Evry, France) - 3 years, Oct. 2020 – Sep. 2023 **
+Information University : https://www.ibisc.univ-evry.fr/equipe/ira2/
+Subject: Deep learning for modelling and training two-wheeled vehicle drivers in Virtual Reality
+Key words: Human Computer Interaction, Virtual reality, Deep learning, Artificial Intelligence, Eye tracking.
+Link: https://www.ibisc.univ-evry.fr/~achellali/files/Sujet_de_these_2020.pdf
+Advisors: Hedi Tabia, Amine Chellali
+Contact: hedi.tabia@univ-evry.fr, amine.chellali@univ-evry.fr
+Dates: Oct. 2020 – Sep. 2023
+Application
+Qualified and interested candidates are invited to send :
+-	a copy of their CV along with a cover letter,
+-	their M1 and M2 academic transcripts,
+-	and 2 recommendation letters
+before May 12, 2020, to:
+-	 Hedi Tabia hedi.tabia@univ-evry.fr and Amine Chellali, amine.chellali@univ-evry.fr
+They are also invited to submit their application through the Paris Saclay University website before May 19 2020.
+** PhD students positions in Machine Learning and Image Analysis at OHSU **
+See link: http://www.lix.polytechnique.fr/Labo/Marie-Paule.Cani/MasterAI/lib/exe/fetch.php?media=2020_04_28_ohsu-ocssb-smmart_-_phd_students_positions.docx
+** Laboratoire IRIMAS, Université de Haute-Alsace - PhD Position in Computer Science
+Optimization of 3D reconstruction using images **
+See links:
+http://www.lix.polytechnique.fr/Labo/Marie-Paule.Cani/MasterAI/lib/exe/fetch.php?media=2020_04_03_these-omega_fr.pdf
+http://www.lix.polytechnique.fr/Labo/Marie-Paule.Cani/MasterAI/lib/exe/fetch.php?media=2020_04_03_these-omega_en.pdf
+Contact: Frederic Cordier <frederic.cordier@uha.fr>
+To apply: application.for.thesis.uha@gmail.com
+**Université Gustave Eiffel – Nantes (France)- GEOLOC _ PHD Student 3 years**
+See links:
+http://www.lix.polytechnique.fr/Labo/Marie-Paule.Cani/MasterAI/doku.php?id=internships&image=2020_phd_offer_geoloc_en.pdf&ns=&tab_details=view&do=media&tab_files=files
+http://www.lix.polytechnique.fr/Labo/Marie-Paule.Cani/MasterAI/doku.php?id=internships&image=2020_these_cifre_geoloc_fr.pdf&ns=&tab_details=view&do=media&tab_files=files
+==== Jobs ====
+** Title  **
+Abstract
+[[Url |text]].
+========= List of internships for 2018-2019 ============
 ==== Master 1 internships (4 to 5 months) ====

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