15 PhD, 4 Postdocs, 9 associate prof. positions (suite)
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last edited by1] Research Associate /Senior Research Associate positions, University of Bristol, UK
[2] Postdoc position in Computational Neuroscience at the CRM, Barcelona, Spain
[3] 1 research associate and 2 funded PhD positions on the evolution of neural learning and plasticity, Loughborough University, UK
[4] Two PhD Student Positions in Computer Vision for Learnings Systems at MPI-IS, Tuebingen, Germany
[5] Postdoctoral Scholars in Computational Brain Science – Brown Institute for Brain Sciences, Providence, RI
[6] Postdoctoral Research Associate in Neural Engineering, University of Essex, UK
PhD and Research Fellows Positions in Machine Learning / Deep Learning, Hochreiter Group, Linz - Austria
[7] 10 PhD Positions in Machine Learning / Deep Learning, Hochreiter Group, Linz, Austria
[8] Five Research Fellows in Machine Learning / Deep Learning, Hochreiter Group, Linz, Austria
[9] Two Research Fellows in Machine Learning / Deep Learning, Hochreiter Group, Linz, Austria
[10] Post doc position in comparative cognition (4-yrs), Vienna, Austria
[11] PhD call for applications PROPOSAL OF SUBJECT OF THESIS - New Stochastic Optimization Approaches to the Aerospace Vehicle Spatial Design Problem
[6] De: Luca Citi <lciti[at]essex.ac.uk>
Postdoctoral Research Associate in Neural Engineering
University of Essex - School of Computer Science and Electronic EngineeringThe Essex Brain-Computer Interfaces and Neural Engineering laboratory is happy to announce a postdoctoral position in the MURI project "Closed-Loop Multisensory Brain-Computer Interface for Enhanced Decision Accuracy" (more information at: https://sites.usc.edu/muri-project/ ).
The project is a very exciting one and we have teamed up with outstanding partners from USC, Harvard, UCL, Berkeley, Imperial, among others.The Essex team's work on the project focuses on brain-computer interfacing, on algorithms for signal processing and extraction of information from EEG and other physiological signals, on behavioural and neuro-physiological investigations of multisensory feature binding and integration, as well as methods for predicting the level of attention and confidence in decision making of a participant from behavioural, physiological and neural data in real time.
The duties of the role include conducting research, development and dissemination of neural engineering techniques within the MURI project.Applicants are expected to hold a PhD (or be very close to submitting their PhD thesis) in Biomedical Engineering, Brain-computer Interfaces, Neural Engineering, Electronic Engineering, Statistics, Physics, Computer Science or a closely related discipline. The ideal candidate will have significant experience in signal processing, statistical modelling of neural signals and processes. Applicants are also expected to have a strong publication record (relative to their career stage) as first author, ideally including publications in 1st quartile journals in relevant areas.
The successful applicant will join the Essex team - formed by Dr Luca Citi (PI), Prof Riccardo Poli (Co-I and UK team leader), Dr Caterina Cinel (named Research Fellow) - and will be part of the Essex BCI-NE Lab, today the UK's largest research group in brain-computer interfaces.
This post is initially fixed-term until the 31st of October 2019 but may be extended for two more years if further funding is approved.
Appointment will be made as Senior Research Officer.
Closes: 30th June 2018
Job Ref: REQ01468
Salary: £32,548 - £34,521 per annumFurther information and application instructions:
https://www.jobs.ac.uk/job/BKF396
[7] De : Ulrich Bodenhofer <bodenhofer[at]bioinf.jku.at>
10 PhD Positions in Machine Learning / Deep Learning, Hochreiter Group, Linz, Austria
Johannes Kepler University Linz (JKU), Austria, is looking for research assistants and junior researchers in the area of machine learning and deep learning with Sepp Hochreiter. These fully-funded positions will be affiliated with the LIT AI Lab and the Institute for Machine Learning andCandidates are expected to enroll into JKU’s PhD programme, and have to option to complete their PhD under Sepp Hochreiter’s supervision.
Job description:
• conduct research in machine learning / deep learning with the aim to obtain a PhD within four years,
• publish in renowned international journals and conferences,
• work in research projects at the LIT AI Lab or in collaboration with partners.Requirements:
• MSc degree or equivalent,
• strong interest and previous education in machine learning (e.g. deep learning, reinforcement learning, kernel methods, probabilistic modeling, meta-learning, attention models); a track record in the field is a plus (e.g. MSc thesis, publications, project experience),
• knowledge in one or more of the following application domains is a plus: signal processing, vision, speech, natural language processing, physics, bio-/chemoinformatics, computational medicine, autonomous driving.About the group: Within the last years, Sepp Hochreiter (who is best known for the invention of LSTM, for the Vanishing Gradient problem, “Flat Minima”, and “Learning to Learn”) has built up a dynamic team of more than20 researchers. The group has recently achieved widely acclaimed contributions and successes, such as, winning the NIH’s Tox21 toxicity prediction challenge with deep learning, the invention of the ELU activation function, Self-Normalizing Networks, and providing a convergence proof for GAN learning. The group has many international collaborations and receives funding from national and international research programs (like the EU) as well as from companies, such as, Johnson&Johnson, Merck, Bayer, Zalando and from joined labs like the Audi.JKU Deep Learning Center.
About the location: The area offers an excellent quality of living in the heart of Europe – close to the alps between Vienna, Salzburg, Prague and Munich. Linz provides a superb cultural environment, most famous for the Ars Electronica Festival, the Brucknerfest, and the nearby Salzburg Festival. The picturesque and versatile landscape provides countless options for recreation and sports in nature (skiing, hiking, climbing, cycling, and many more).
If you have questions, please contact: Prof. Dr. Sepp Hochreiter, +43 732 2468 4520, recruitment[at]bioinf.jku.at.
[8] De : Ulrich Bodenhofer <bodenhofer[at]bioinf.jku.at>
Five Research Fellows in Machine Learning / Deep Learning, Hochreiter Group, Linz, Austria
Johannes Kepler University Linz (JKU), Austria, is looking for five post-doctoral research fellows to advance machine learning and deep learning in close collaboration with Sepp Hochreiter. These positions are affiliated with the LIT AI Lab and the Institute for Machine Learning.
Job description:
• conduct independent research in the field,
• collaborate in machine learning and deep learning projects,
• publish in renowned international journals and conferences.Requirements:
• PhD degree,
• track record in machine learning (e.g. deep learning, reinforcement learning, kernel methods, probabilistic modeling, meta-learning, attention models),
• knowledge in one or more of the following application domains is a plus: signal processing, vision, speech, natural language processing, physics, bio-/chemoinformatics, computational medicine, autonomous driving,
• willingness and ability to work in a team.About the group: Within the last years, Sepp Hochreiter (who is best known for the invention of LSTM, for the Vanishing Gradient problem, “Flat Minima”, and “Learning to Learn”) has built up a dynamic team of more than20 researchers. The group has recently achieved widely acclaimed contributions and successes, such as, winning the NIH’s Tox21 toxicity prediction challenge with deep learning, the invention of the ELU activation function, Self-Normalizing Networks, and a convergence proof for GAN learning. The group has many international collaborations and receives funding from national and international research programsas well as from companies, such as, Johnson&Johnson, Merck, Bayer, Zalando and from joined labs like the Audi.JKU Deep Learning Center.
About the location: The area offers an excellent quality of living in the heart of Europe – close to the alps between Vienna, Salzburg, Prague and Munich. Linz provides a superb cultural environment, most famous for the Ars Electronica Festival, the Brucknerfest, and the nearby Salzburg Festival. The picturesque and versatile landscape provides countless options for recreation and sports in nature (skiing, hiking, climbing, cycling, and many more).
If you have questions, please contact: Prof. Dr. Sepp Hochreiter, +43 732 2468 4520, recruitment[at]bioinf.jku.at.
[9] De : Ulrich Bodenhofer <bodenhofer[at]bioinf.jku.at>
Two Research Fellows in Machine Learning / Deep Learning, Hochreiter Group, Linz, Austria
Johannes Kepler University Linz (JKU), Austria, is looking for two post-doctoral research fellows to advance machine learning and deep learning research with Sepp Hochreiter. These six year positions are affiliated both with the newly established LIT AI Lab and the Institute for Machine Learning.
Job description:
• conduct independent research in the field,
• collaborate in machine learning and deep learning projects,
• publish in renowned international journals and conferences,
• supervise students; prepare and hold lectures; support study programs.Requirements:
• PhD degree,
• track record in machine learning (e.g. deep learning, reinforcement learning, kernel methods, probabilistic modeling, meta-learning, attention models),
• knowledge in one or more of the following application domains is a plus: signal processing, vision, speech, natural language processing, physics, bio-/chemoinformatics, computational medicine, autonomous driving,
• willingness and ability to work in a team and to support students and junior researchers.About the group: Within the last years, Sepp Hochreiter (who is best known for the invention of LSTM, for the Vanishing Gradient problem, “Flat Minima”, and “Learning to Learn”) has built up a dynamic team of more than 20 researchers. The group has recently achieved widely acclaimed contributions and successes, such as, winning the NIH’s Tox21 toxicity prediction challenge with deep learning, the invention of the ELU activation function, Self-Normalizing Networks, and providing a convergence proof for GAN learning. The group has many international collaborations and receives funding from national and international research programs as well as from companies, such as, Johnson&Johnson, Merck, Bayer, Zalando and from joined labs like the Audi.JKU Deep Learning Center.
About the location: The area offers an excellent quality of living in the heart of Europe – close to the alps between Vienna, Salzburg, Prague and Munich. Linz provides a superb cultural environment, most famous for the Ars Electronica Festival, the Brucknerfest, and the nearby Salzburg Festival. The picturesque and versatile landscape provides countless options for recreation and sports in nature (skiing, hiking, climbing, cycling, and many more).
If you have questions, please contact: Prof. Dr. Sepp Hochreiter, +43 732 2468 4520, recruitment[at]bioinf.jku.at.
Prospective applicants interested in these positions are requested to electronically send an application via the online portal http://jku.at/application. Please include “Job Reference Number 3619” (deadline: July 30, 2018) or “Job Reference Number 3578” (deadline: July 4, 2018).
[10] De: Isabelle Charrier <isabelle.charrier[at]u-psud.fr>
Post doc position in comparative cognition (4-yrs), Vienna, Austria
At the Unit of Comparative Cognition, Messerli Research Institute, Vienna, Austria, we are seeking a postdoctoral researcher who is eager to investigate cognitive and emotional processes in non-human animals, especially dogs. At the Clever Dog Lab we are committed to researching the behavioural, physiological (including neuronal) and genetic underpinnings of dog cognition. The successful candidate will have the opportunity to develop her/his own research agenda, using a large repertoire of state-of-the-art techniques and methodologies (including fMRI, eye-tracking, touch screens, automatized video analysis and behaviour annotation) and benefiting from administrative and technical support from members of the unit (including a lab manager, mechanical and electronic technicians and IT personnel).
Please see the full advertisement here:
http://www.vetmeduni.ac.at/fileadmin/v/z/mitteilungsblatt/stellen/2017_2018/20180615_Postdoc_Comparative_Cognition.pdfMore details about the unit of Comparative Cognition, Vienna:http://www.vetmeduni.ac.at/en/messerli/science/cognition
Informal enquiries about this post may be directed to Professor Ludwig Huber, ludwig.huber[at]vetmeduni.ac.at
Application deadline: 8th July 2018.
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Prof. Ludwig Huber, PhD
Head of Comparative Cognition
Messerli Research InstituteUniversity of Veterinary Medicine, Vienna (Vetmeduni Vienna)
Veterinaerplatz 1, 1210 Vienna, Austria
T +43 1 25077-2680
M +43 664 60257-6250
ludwig.huber[at]vetmeduni.ac.at
www.vetmeduni.ac.at/messerliPartner institutions of the Messerli Research Institute:
Messerli-Foundation, University of Veterinary Medicine Vienna, Medical University of Vienna, University of Vienna
[11] De : Rachid Chelouah <rc[at]eisti.eu>
PhD call for applications PROPOSAL OF SUBJECT OF THESIS - Reference: TIS-DTIS-2018-15
Title: New Stochastic Optimization Approaches to the Aerospace Vehicle Spatial Design Problem
The host laboratory at ONERA : Domain : TIS
Department: Information Processing and Systems Department - Unit: Design and Evaluation of Aerospace Vehicles
Location (ONERA center): PalaiseauONERA responsibles: Tel. 01 80 38 66 30 Email :
Romain Wuilbercq romain.wuilbercq[at]onera.fr
Karim Dahia karim.dahia[at]onera.fr
Arnault Tremolet arnault.tremolet[at]onera.frThe host laboratory at Paris-Seine : Quartz
Thesis supervisor: Rachid Chelouah Email : rachid.chelouah[at]eisti.eu Tel. :+33 (0)1 34 25 84 20Co-encadrant: Stefan Borhofen Email : stefan.bornhofen[at]eisti.eu
Address: University of Paris-Seine/EISTI, 95011 Cergy-Pontoise CedexDescription of the subject
The pre-project phase for the development of an aerospace vehicle is one that is likely to bring out innovative configurations. In a general way, after a parallel evaluation of several topologies, a particular configuration is chosen on the basis of selection criteria resulting from the optimization of a mission. At present no systematization of the process seems to be possible without having a function of planning in the sense of geometric placement of objects. The problem of the geometric arrangement is defined by the ability to place different objects without interpenetration in an envelope. The generalization of the problem consists in taking into account their functional aspect which can contribute to prohibit or force their relative placement.The work focuses on objects and an external topology of 3D vehicle. To solve the problem of geometrical arrangement, two large families stand out, one of excluding all solutions with interpenetrations which supposes the entire evaluated, one speaks of legal placement, the other by authorizing them but by affecting a penalty function according to the degree of interpenetration, this is called relaxed placement. The latter approach, adopted as part of the proposed work, was exploited in [1] highlighting the interest of the coupling between robust multi-objective optimization techniques and a separation method.
The first part of the work consists in appropriating methods of modeling and rapid evaluation of collisions between objects. Given the chosen relaxed placement, an assessment of this collision should be made by estimating the interpenetration depth, for example. The physical aspects (eg electromagnetic field, radiative radiation) and functional aspects can also be introduced by the notion of region of influence [2]. The consideration of spatial collisions and regions of influence in a planning process will be explored during this thesis.Such a problem of placement of objects is characterized by a strong combinatorics. The result is the need to explore a vast space of solutions. The number of variables associated with the quantity of objects to be placed is also important. Finally, the integration of constraints, to be defined exhaustively, is the heart of the problem because of their diverse nature (geometric, functional, thermal, ... etc). Stochastic optimization algorithms therefore appear relevant in order to explore a vast space of solutions, containing many constraints and many variables (complete Np problem) [3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13]. The challenge of this thesis will be to adapt optimization algorithms to the specificity of the problem by introducing for example an algorithmic overlay so as to deal with all constraints.
The developments of this thesis will be carried out in a capitalization environment such as the ACADIA platform developed at ONERA. In particular, they will be applied to multidisciplinary optimization problems using the OpenMDAO framework (Python, Cython, C ++). The methods and algorithms developed will be applied to various aerospace applications or road and rail transport that will verify their genericity.
References :
[1] G. Jacquenot, « Méthode générique pour l’optimisation d’agencement géométrique et fonctionnel », 18 Janvier 2010.
[2] Joaquim P. L. Viegas, Susana M. Vieira, Joao M. C. Sousa and, Elsa M. P. Henriques, « Metaheuristics for the 3D Bin Packing Problem in HAPE3D the Steel Industry », 2014 IEEE Congress on Evolutionary Computation (CEC) July 6-11, 2014, Beijing, China.
[3] Xiao LIU, Jia-min LIU, An-xi CAO, Zhuang-le YAO, « - a new constructive algorithm for the 3D irregular packing problem », Front Inform Technol Electron Eng 16(5):380-390, 2015
[4] Marouene Kefi, Paul Richard, Thuong Hoang, Takehiko Yamaguchi and Vincent Barichard, « Using Constraint Solver for 3D Layout Assistance in Human-scale Virtual Environment », HUCAPP 2017 - International Conference on Human Computer Interaction Theory and Applications, 2017.
[5] Giorgio Fasano, « A global optimization point of view to handle non-standard object packing problems », J Glob Optim, 2012.
[6] C. Monjaret, « Introduction aux méthodes d’optimisation pour l’aménagement spatial », RT 1/14706 DPRS, 2010.
[7] C. Leboucher, R. Chelouah, H.S. Shin, S. Le Ménec, P. Siarry, A. Tsourdos and A. Kotenkoff, An Enhanced Particle Swarm Optimisation Method Integrated With Evolutionary Game Theory, IEEE Transactions on Computational Intelligence and AI in Games, Janv 2018
[8] Peio Loubiere, Astrid Jourdan, Patrick Siarry and Rachid Chelouah, A modified sensitivity analysis method for driving a multidimensional search in the Artificial Bee Colony algorithm, IEEE Congress on Evolutionary Computation, IEEE CEC 2016,
[9] C. Leboucher, H.S. Shin, P. Siarry, S. Le Ménec, R. Chelouah, and A. Tsourdos, Convergence Proof of an Enhanced Particle Swarm Optimisation Method Integrated with Evolutionary Game Theory, Information Sciences, ScienceDirect, Elsevier, DOI doi:10.1016/j.ins.2016.01.011, pp. 389-411 2016:
[10] James Kennedy, Rachid Chelouah, Maurice Clerc et Patrick Siarry, Swarm Intelligence Research édité en deux tomes par IGI Publishing ISSN 1947-9263 et ISSN 1947-9271, IJSIR, 2012.Profile of candidate du (de la) candidat (e) :
Education: Student in Master 2 or engineering school
Desired Specificities: Mathematical Modeling, Stochastic Optimization, Artificial Intelligence, Computer Science.Person to contact :
Rachid Chelouah
Laboratoire Quartz
Université Paris-Seine / EISTI
Mail : rc[at]eisti.euKarim Dahia
ONERA
Laboratoire : Conception et Évaluation de Véhicules Aérospatiaux
mail : karim.dahia[at]onera.fr
