The Multimodal Signal Analysis and Enhancement (MuSAE) Lab,
located at the Institut National de la Recherche Scientifique
(INRS), University of Quebec, in beautiful Montreal, Canada is
looking to fill the position of a postdoctoral fellow to work on a
project related to EEG signal enhancement. The ideal candidate
will have a background in digital signal processing and hands-on
experience with classical EEG denoising methods, as well as
experience developing new artefact removal algorithms. Experience
with Matlab and/or Python programming is a must, and hands-on
experience with parallel computing, deep learning, and mobile EEG
devices are considered assets. A strong publication record in
renowned journals and conferences is a must.
At the MuSAE Lab, we are building next-generation context-aware
human-machine interfaces and the fellow will have the opportunity
to interact with other group members working on multimodal
brain-computer interfaces (NIRS-EEG), objective biosignal quality
measurement, affective BCIs, amongst other related projects. The
Lab also works closely with NeuroTechMTL
(http://mtl.neurotechx.com) to raise awareness about
neurotechnology and promote innovation in the related fields
through teaching, mentorship and networking. For more details,
please visit http://musaelab.ca/about/
Interested and qualified applicants are invited to email Prof.
Tiago H. Falk at falk@emt.inrs.ca with their complete CV, name and
contact information of three references, and copies of three most
recent (relevant) papers. Applications will be received until June
3rd and the position will start as soon as possible. Salary will
be in line with the scale suggested by the Natural Sciences and
Engineering Research Council of Canada and commensurate with
experience.
Sincerely,
--
Tiago H. Falk
Associate Professor, INRS-EMT
Director, MuSAE Lab
Web: http://musaelab.ca
Twitter: @MuSAELab
Kamis, 02 Juni 2016
Sabtu, 28 Mei 2016
A fully funded 4-5 years PhD position in Mathematical Statistics at Lund University, Sweden.
A fully funded 4-5 years PhD position in Mathematical Statistics is available at Lund University, Sweden.
In
many classical statistical applications, likelihood inference is often
the preferred method for statistical analysis for many and well known
reasons. However model complexity, such as nonlinear dynamics and/or
latent states, can make it difficult (if not outright impossible) to
pursue the likelihood in explicit mathematical form. Recent years have
provided breakthroughs in computational statistics for dealing with
models having intractable likelihoods (examples are approximate Bayesian
computation (ABC) and methods based on sequential Monte Carlo).
Aim
of the research project is to further develop these statistical methods
(particularly in the Bayesian framework) and use them in applications
to e.g. biomedical research. In fact the project has some degree of
interdisciplinarity and is also concerned with stochastic dynamic models
for protein folding described by stochastic differential equations.
Protein folding is an important biological process which is also
associated with a wide range of human diseases.
Applicants
with an interest in Bayesian statistics, Monte Carlo methods, or
stochastic differential equations are particularly encouraged to apply.
You
will be based at the Centre for Mathematical Sciences at Lund
University, in the south of Sweden. In addition to develop your research
in statistical methods you will collaborate with experts who have
domain-specific expertise in protein dynamics and biostatistical
modelling at Copenhagen University.
The project
is supervised by Dr. Umberto Picchini and offers the scope for personal
development to students who are interested in computational and applied
statistics. Informal enquiries should be sent to Umberto Picchini (umberto@maths.lth.se).
Instructions on how to apply and eligibility/entry requirements are available at https://lu.mynetworkglobal.
Closing date for applications is 9 June 2016.
ps:
Although in the post it is mentioned 1st October 2016 as the starting
date for the position there is some room for negotiation.
Jumat, 13 Mei 2016
PhD positions in "Robust Speech Encoding in Impaired Hearing", Uni Oldenburg, Germany
The positions are part of the ERC project RobSpear "Robust Speech
Encoding in Impaired Hearing" that takes an interdisciplinary approach
(combining computational auditory models with EEG, psychoacoustics and
machine-learning) to study sound processing in listeners with impaired
hearing due to ageing or noise exposure. Both novel diagnostic methods
as well as signal-processing strategies for hearing restoration are the
focus of this project. Patient contact is required. Funding is available
for three years and projects start in the fall of 2016.
The Physiology and Modeling of Auditory Perception Group headed by Prof.
Dr. Sarah Verhulst at Oldenburg University (DE) offers an international
scientific environment as well as access to world-class research
facilities via hearing4all.eu For more information about the group see
<http://www.uni-oldenburg.de/
Applicants for the position must hold an academic university degree
(master or equivalent). Candidates should have excellent oral and
written English skills, experience with signal processing and coding
(Matlab, Phython).
Project 1: Model-based hearing diagnostics
The focus is on computational auditory modeling as well as optimizing
(multichannel) EEG and otoacoustic emission methods. Background is a
M.Sc. in neuroscience, or acoustics, physics, electrical engineering,
experimental psychology. Experience with EEG recording (writing
experiment code and analysis) is necessary. Auditory modeling experience
is a plus.
Project 2: Psychoacoustic sound encoding strategies
Experience with conducting psychoacoustic experiments is necessary
(writing experiment code and analysis). Background is a M.Sc. in
neuroscience, or acoustics, physics, electrical engineering,
experimental psychology, audiology.
Full information about the positions and application details can be
found here (application Deadline: May 30th 2016):
http://www.uni-oldenburg.de/
--
---
Prof. Dr. Sarah Verhulst
Cluster of Excellence Hearing4all
Dept. of Medical Physics and Acoustics
Oldenburg University
Office: W30 - 106
Encoding in Impaired Hearing" that takes an interdisciplinary approach
(combining computational auditory models with EEG, psychoacoustics and
machine-learning) to study sound processing in listeners with impaired
hearing due to ageing or noise exposure. Both novel diagnostic methods
as well as signal-processing strategies for hearing restoration are the
focus of this project. Patient contact is required. Funding is available
for three years and projects start in the fall of 2016.
The Physiology and Modeling of Auditory Perception Group headed by Prof.
Dr. Sarah Verhulst at Oldenburg University (DE) offers an international
scientific environment as well as access to world-class research
facilities via hearing4all.eu For more information about the group see
<http://www.uni-oldenburg.de/
Applicants for the position must hold an academic university degree
(master or equivalent). Candidates should have excellent oral and
written English skills, experience with signal processing and coding
(Matlab, Phython).
Project 1: Model-based hearing diagnostics
The focus is on computational auditory modeling as well as optimizing
(multichannel) EEG and otoacoustic emission methods. Background is a
M.Sc. in neuroscience, or acoustics, physics, electrical engineering,
experimental psychology. Experience with EEG recording (writing
experiment code and analysis) is necessary. Auditory modeling experience
is a plus.
Project 2: Psychoacoustic sound encoding strategies
Experience with conducting psychoacoustic experiments is necessary
(writing experiment code and analysis). Background is a M.Sc. in
neuroscience, or acoustics, physics, electrical engineering,
experimental psychology, audiology.
Full information about the positions and application details can be
found here (application Deadline: May 30th 2016):
http://www.uni-oldenburg.de/
--
---
Prof. Dr. Sarah Verhulst
Cluster of Excellence Hearing4all
Dept. of Medical Physics and Acoustics
Oldenburg University
Office: W30 - 106
Jumat, 06 Mei 2016
Istituto Italiano di Tecnologia – Machine Learning and Robotics PhD Positions and Scholarship
Machine Learning and Robotics PhD positions (with scholarships) are
available at the iCub Facility and the Laboratory for Computational and
Statistical Learning (LCSL, IIT@MIT), Istituto Italiano di Tecnologia
(IIT). See list of topics below.
Check the official https://www.iit.it/phd-school. and the tips+tricks section for the detailed instructions on how to apply.
Applications must be filed through the University of Genoa using the online service at this link: https://www.studenti.unige.it/
——————————
2) Scene analysis using deep-learning
Tutor: Lorenzo Natale, Lorenzo Rosasco
Description: machine learning, and in particular deep learning methods, have been applied with remarkable success to solve visual problems like pedestrian detection, object retrieval, recognition and segmentation. One of the difficulties with these techniques is that training requires a large amount of data and it is not straightforward to adopt them when training samples are acquired online and autonomously by a robot. One solution is to adopt pre-trained convolutional neural networks (DCNN) for image representation and use simpler classifiers, either in batch or incrementally. Following this approach DCNNs have been integrated in the iCub visual system leading to a remarkable increase of object recognition performance. However, scene analysis in realistic settings is still challenging due to scale, light variability and clutter. The goal of this project is to further investigate and improve the iCub recognition and visual segmentation capabilities. To this aim we will investigate techniques for pixel-base semantic segmentation using DCNNs and object detection mixing top-down and bottom-up cues for image segmentation.
Requirements: This PhD project will be carried out within the Humanoid Sensing and Perception lab (iCub Facility) and Laboratory for Computational and Statistical Learning. The ideal candidate should have a degree in Computer Science or Engineering (or equivalent) and background in Machine Learning, Robotics and possibly in Computer Vision. He should also be highly motivated to work on a robotic platform and have strong computer programming skills.
References:
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei, ImageNet Large Scale Visual Recognition Challenge, arXiv:1409.0575, 2014
Jon Long, Evan Shelhamer, Trevor Darrell, Fully Convolutional Networks for Semantic Segmentation, CVPR 2015
Pasquale, G., Ciliberto, C., Odone, F., Rosasco, L., and Natale, L., Teaching iCub to recognize objects using deep Convolutional Neural Networks, in Proc. 4th Workshop on Machine Learning for Interactive Systems, 2015
3) Implicit learning
Tutor: Lorenzo Natale, Lorenzo Rosasco
Description: machine learning, and in particular deep learning methods, have been applied with remarkable success to solve visual problems like pedestrian detection, object retrieval, recognition and segmentation. One of the difficulties with these techniques it that training requires a large amount of labelled data and it is not straightforward to adopt them when training samples are acquired online and autonomously by the robot. Critical issues are how to obtain large amount of training samples, how to perform object segmentation and labelling. They key idea is develop weakly supervised frameworks, where learning can exploit forms of implicit
labelling. In previous work we have proposed to exploit coherence between perceived motion and the robot own-motion to autonomously learn a visual detector of the hand. The goal of this project is to investigate algorithms for learning to recognize object by exploiting implicit supervision, focusing in particular on the strategies that allow the robot to extract training samples autonomously, starting from motion and disparity cues.
Requirements: This PhD project will be carried out within the Humanoid Sensing and Perception lab (iCub Facility) and Laboratory for Computational and Statistical Learning. The ideal candidate should have a degree in Computer Science or Engineering (or equivalent) and background in Machine Learning, Robotics and possibly in Computer Vision. He should also be highly motivated to work on a robotic platform and have strong computer programming skills.
References:
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei, ImageNet Large Scale Visual Recognition Challenge, arXiv:1409.0575, 2014.
Wang, X., Gupta, A., Unsupervised Learning of Visual Representations using Videos, arXiv:1505.00687v2, 2015.
Ciliberto, C., Smeraldi, F., Natale, L., Metta, G., Online Multiple Instance Learning Applied to Hand Detection in a Humanoid Robot, IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, California, September 25-30, 2011.
4) Learning to recognize objects using multimodal cues
Tutor: Lorenzo Natale, Lorenzo Rosasco
Description: robots can actively sense the environment using not only vision but also haptic information. One of the problems to be addressed in this case is how to control the robot to explore the environment and extract relevant information (so called exploratory procedures). Conventionally, learning to recognize objects has been primarily addressed using vision. However, physical properties of objects are more directly perceived using other sensory modalities. For this reason, recent work has started to investigate how to discriminate objects using other sensory channels, like touch, force and proprioception. The goals of this project are i) to implement control strategies for object exploration, investigating to what extent different
explorative strategies contribute to object discrimination, ii) the implementation of learning algorithms that allow the robot to discriminate objects using haptic information and, finally, iii) to investigate how haptic information can be integrated with vision to build a rich model of the objects for better discrimination.
Requirements: This PhD project will be carried out within the Humanoid Sensing and Perception lab (iCub Facility) and Laboratory for Computational and Statistical Learning. The ideal candidate should have a degree in Computer Science or Engineering (or equivalent) and background in Machine Learning, Robotics and possibly in Computer Vision. He should also be highly motivated to work on a robotic platform and have strong computer programming skills.
References:
Pasquale, G., Ciliberto, C., Odone, F., Rosasco, L., and Natale, L., Teaching iCub to recognize objects using deep Convolutional Neural Networks, in Proc. 4th Workshop on Machine Learning for Interactive Systems, 2015.
Liarokapis, M.V., Çalli, B., Spiers, A.J, Dollar, A.M., Unplanned, model-free, single grasp object classification with underactuated hands and force sensors, IROS, 2015.
Madry, M., Bo, L., Kragic, D. and Fox, D., ST-HMP: Unsupervised Spatio-Temporal feature learning for tactile data, ICRA 2014.
12) Tera-Scale Machine Learning in a Dynamic World
Tutors: Giorgio Metta, Lorenzo Rosasco
Department: iCub Facility
Description: Machine learning methods have been applied with remarkable success to solve a variety of perceptual/cognitive tasks, e.g. in vision and speech. Yet, current state of the art algorithms are challenged by increasing amount of high-dimensional data, possibly evolving over time. The goal of this project is to tackle the design and deployment of sound machine learning methods to learn from large/huge scale data sets possibly acquired over time in changing conditions. This problem finds natural application in the context of humanoid robotics where data are constantly made available by sensory systems. The plan is to first consider extensions of classical convex approaches such as kernel methods, by incorporating ideas such as sketching, hashing and randomization. The idea is then to further explore possibly non-convex, hierarchical (deep) learning methods, based on greedy optimization strategy. Emphasis will be on developing effective solutions, while keeping optimal theoretical guarantees.
Requirements: This PhD project will be carried out within the iCub Facility and Laboratory for Computational and Statistical Learning. The ideal candidate should have a degree in Computer Science or Engineering (or equivalent) and background in Machine Learning, Robotics and in optimization/signal processing. He should also be highly motivated to work on a robotic platform and have strong mathematical and or/ computer programming skills.
References:
Incremental Semiparametric Inverse Dynamics Learning, Raffaello Camoriano, Silvio Traversaro, Lorenzo Rosasco, Giorgio Metta, Francesco Nori (IROS 2016)
Less is More: Nyström Computational Regularization Alessandro Rudi, Raffaello Camoriano, Lorenzo Rosasco (NIPS 2015)
Contacts: Giorgio Metta (giorgio[dot]metta[at]iit.it) and Lorenzo Rosasco (lrosasco[at]mit.edu)
Check the official https://www.iit.it/phd-school. and the tips+tricks section for the detailed instructions on how to apply.
Applications must be filed through the University of Genoa using the online service at this link: https://www.studenti.unige.it/
——————————
2) Scene analysis using deep-learning
Tutor: Lorenzo Natale, Lorenzo Rosasco
Description: machine learning, and in particular deep learning methods, have been applied with remarkable success to solve visual problems like pedestrian detection, object retrieval, recognition and segmentation. One of the difficulties with these techniques is that training requires a large amount of data and it is not straightforward to adopt them when training samples are acquired online and autonomously by a robot. One solution is to adopt pre-trained convolutional neural networks (DCNN) for image representation and use simpler classifiers, either in batch or incrementally. Following this approach DCNNs have been integrated in the iCub visual system leading to a remarkable increase of object recognition performance. However, scene analysis in realistic settings is still challenging due to scale, light variability and clutter. The goal of this project is to further investigate and improve the iCub recognition and visual segmentation capabilities. To this aim we will investigate techniques for pixel-base semantic segmentation using DCNNs and object detection mixing top-down and bottom-up cues for image segmentation.
Requirements: This PhD project will be carried out within the Humanoid Sensing and Perception lab (iCub Facility) and Laboratory for Computational and Statistical Learning. The ideal candidate should have a degree in Computer Science or Engineering (or equivalent) and background in Machine Learning, Robotics and possibly in Computer Vision. He should also be highly motivated to work on a robotic platform and have strong computer programming skills.
References:
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei, ImageNet Large Scale Visual Recognition Challenge, arXiv:1409.0575, 2014
Jon Long, Evan Shelhamer, Trevor Darrell, Fully Convolutional Networks for Semantic Segmentation, CVPR 2015
Pasquale, G., Ciliberto, C., Odone, F., Rosasco, L., and Natale, L., Teaching iCub to recognize objects using deep Convolutional Neural Networks, in Proc. 4th Workshop on Machine Learning for Interactive Systems, 2015
3) Implicit learning
Tutor: Lorenzo Natale, Lorenzo Rosasco
Description: machine learning, and in particular deep learning methods, have been applied with remarkable success to solve visual problems like pedestrian detection, object retrieval, recognition and segmentation. One of the difficulties with these techniques it that training requires a large amount of labelled data and it is not straightforward to adopt them when training samples are acquired online and autonomously by the robot. Critical issues are how to obtain large amount of training samples, how to perform object segmentation and labelling. They key idea is develop weakly supervised frameworks, where learning can exploit forms of implicit
labelling. In previous work we have proposed to exploit coherence between perceived motion and the robot own-motion to autonomously learn a visual detector of the hand. The goal of this project is to investigate algorithms for learning to recognize object by exploiting implicit supervision, focusing in particular on the strategies that allow the robot to extract training samples autonomously, starting from motion and disparity cues.
Requirements: This PhD project will be carried out within the Humanoid Sensing and Perception lab (iCub Facility) and Laboratory for Computational and Statistical Learning. The ideal candidate should have a degree in Computer Science or Engineering (or equivalent) and background in Machine Learning, Robotics and possibly in Computer Vision. He should also be highly motivated to work on a robotic platform and have strong computer programming skills.
References:
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei, ImageNet Large Scale Visual Recognition Challenge, arXiv:1409.0575, 2014.
Wang, X., Gupta, A., Unsupervised Learning of Visual Representations using Videos, arXiv:1505.00687v2, 2015.
Ciliberto, C., Smeraldi, F., Natale, L., Metta, G., Online Multiple Instance Learning Applied to Hand Detection in a Humanoid Robot, IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, California, September 25-30, 2011.
4) Learning to recognize objects using multimodal cues
Tutor: Lorenzo Natale, Lorenzo Rosasco
Description: robots can actively sense the environment using not only vision but also haptic information. One of the problems to be addressed in this case is how to control the robot to explore the environment and extract relevant information (so called exploratory procedures). Conventionally, learning to recognize objects has been primarily addressed using vision. However, physical properties of objects are more directly perceived using other sensory modalities. For this reason, recent work has started to investigate how to discriminate objects using other sensory channels, like touch, force and proprioception. The goals of this project are i) to implement control strategies for object exploration, investigating to what extent different
explorative strategies contribute to object discrimination, ii) the implementation of learning algorithms that allow the robot to discriminate objects using haptic information and, finally, iii) to investigate how haptic information can be integrated with vision to build a rich model of the objects for better discrimination.
Requirements: This PhD project will be carried out within the Humanoid Sensing and Perception lab (iCub Facility) and Laboratory for Computational and Statistical Learning. The ideal candidate should have a degree in Computer Science or Engineering (or equivalent) and background in Machine Learning, Robotics and possibly in Computer Vision. He should also be highly motivated to work on a robotic platform and have strong computer programming skills.
References:
Pasquale, G., Ciliberto, C., Odone, F., Rosasco, L., and Natale, L., Teaching iCub to recognize objects using deep Convolutional Neural Networks, in Proc. 4th Workshop on Machine Learning for Interactive Systems, 2015.
Liarokapis, M.V., Çalli, B., Spiers, A.J, Dollar, A.M., Unplanned, model-free, single grasp object classification with underactuated hands and force sensors, IROS, 2015.
Madry, M., Bo, L., Kragic, D. and Fox, D., ST-HMP: Unsupervised Spatio-Temporal feature learning for tactile data, ICRA 2014.
12) Tera-Scale Machine Learning in a Dynamic World
Tutors: Giorgio Metta, Lorenzo Rosasco
Department: iCub Facility
Description: Machine learning methods have been applied with remarkable success to solve a variety of perceptual/cognitive tasks, e.g. in vision and speech. Yet, current state of the art algorithms are challenged by increasing amount of high-dimensional data, possibly evolving over time. The goal of this project is to tackle the design and deployment of sound machine learning methods to learn from large/huge scale data sets possibly acquired over time in changing conditions. This problem finds natural application in the context of humanoid robotics where data are constantly made available by sensory systems. The plan is to first consider extensions of classical convex approaches such as kernel methods, by incorporating ideas such as sketching, hashing and randomization. The idea is then to further explore possibly non-convex, hierarchical (deep) learning methods, based on greedy optimization strategy. Emphasis will be on developing effective solutions, while keeping optimal theoretical guarantees.
Requirements: This PhD project will be carried out within the iCub Facility and Laboratory for Computational and Statistical Learning. The ideal candidate should have a degree in Computer Science or Engineering (or equivalent) and background in Machine Learning, Robotics and in optimization/signal processing. He should also be highly motivated to work on a robotic platform and have strong mathematical and or/ computer programming skills.
References:
Incremental Semiparametric Inverse Dynamics Learning, Raffaello Camoriano, Silvio Traversaro, Lorenzo Rosasco, Giorgio Metta, Francesco Nori (IROS 2016)
Less is More: Nyström Computational Regularization Alessandro Rudi, Raffaello Camoriano, Lorenzo Rosasco (NIPS 2015)
Contacts: Giorgio Metta (giorgio[dot]metta[at]iit.it) and Lorenzo Rosasco (lrosasco[at]mit.edu)
University of Trento – PhD student positions in wireless sensor networks and the Internet of Things
Applications are invited from those interested in pursuing a PhD on wireless sensor networks (WSNs) and the Internet of Things (IoT) at the University of Trento, Italy, in the D3S group. D3S is a cross-institution research group focusing on dynamic, decentralized, distributed systems.
Wireless sensor networks are considered one of the key technologies enabling the Internet of Things vision. In this context, the D3S group has been particularly successful in bringing research results into real-world, long-term, operational deployments. Examples include the structural health monitoring of a medieval tower, the closed-loop control of lighting in a road tunnel, and geo-referenced proximity monitoring of wildlife. The scientific results of these projects received numerous awards, e.g., at IPSN (2009, 2011, 2015) and PerCom (2012).
Although we emphasize real-world applications as a motivation and a concrete opportunity for the validation of our research, the latter is not limited to the immediate needs of deployments. We perform a mix of curiosity-driven and application-driven research. The research challenges tackled by D3S span a broad set of topics, ranging from low-layer issues concerned with the characterization and design of communication protocols to higher-layer issues related with programming platforms and software architectures.
New PhD students are invited to participate in ongoing projects to gain experience and insight into real systems, and to identify novel, challenging problems whose solutions break new grounds. Examples of potential research topics, broadly defined, include:
* communication protocols and abstractions for new wireless technologies that are disrupting the current landscape of WSNs and IoT, e.g., by enabling long-range communication (LoRa), low-power ranging (UWB), or commonplace proximity detection (BLE);
* programming models, middleware, and protocols to efficiently disseminate, store, and retrieve information in a context-aware fashion in a smart city IoT scenario;
* exploiting synchronous wireless transmissions for efficient and predictable actuation in large-scale control infrastructures.
The language of the research group is English.
The D3S group, and Trento at large, provide a fertile environment for high-quality research: two of our PhD students received the Best Ph.D. Thesis Award at the European Conference on Wireless Sensor Networks (EWSN) in 2009 and 2012.
The Department of Information Engineering and Computer Science (DISI) is a leading and fast-growing research institution, characterized by a young and international faculty and by a large, international student population. Indicators for scientific production place the department among the top in Europe. The department and the PhD school closely collaborate with – and operate in – a rapidly growing research and innovation environment
characterized by top class research centers and an increasing number of industrial research labs.
Trento is a vibrant city with a beautifully preserved historic center, consistently ranked among the best cities for quality of life in Italy. It offers a variety of cultural and sports opportunities all year around, as well as excellent food and wine.
Applications must be filed online before May 26, 2016 (at 16:00, Italy time) at the link below.
IMPORTANT:
In the application, under “Research areas of Interest” you must select BOTH “area B (Computer Science)” AND “area D (Telecommunications)” as your preferred ones.
**Failure to do so may result in ineligibility for these positions**
It does not matter which one you chose as first and second choice, and you can use the same statement of purpose for both. The keyword of reference for area B is “Systems and Networks”, while for area D is “Distributed Sensing”.
It is strongly advised to contact Prof. Gian Pietro Picco (gianpietro[dot]picco[at]unitn[dot]it) well before submitting the application, by providing a curriculum vitae including three references.
Links:
Call for application: http://ict.unitn.it/
Online PhD Application: http://www.unitn.it/en/apply/
Prof. Gian Pietro Picco: http://disi.unitn.it/~picco
D3S Group and Projects: http://d3s.disi.unitn.it
PhD School: http://ict.unitn.it/
DISI: http://disi.unitn.it
Sabtu, 30 April 2016
15 PhD Scholarships European Joint Doctorate programme on Product, Energy and Resource Recovery from Wastewater
We
are hiring! European Joint Doctorate programme on Product, Energy and
Resource Recovery from Wastewater SuPER-W is able to offer 15 PhD
scholarships (duration 3 year). International programme organised by 5
renowned European universities (Ghent University (Belgium), TU Delft
(the Netherlands), RWTH Aachen University (Germany), University of
Chemistry and Technology Prague (Czech Republic), and Politècnica de
Catalunya-BarcelonaTech (Spain)). Deadline for application Application
deadline: 15 May 2016. Important: candidates must obtain their MSc no
later than July 2016.
OPEN PHD POSITION AT BELL LABS FRANCE / EURECOM IN MACHINE LEARNING / GAME THEORY
The Mathematics of Complex and Dynamic Networks Department of Alcatel-Lucent Bell Labs France and the Data Science Department of EURECOM invite applications for a fully funded PhD position on “Learning in Blotto games and applications to modeling attention in social networks.” The PhD will be co-supervised by Dr. Alonso Silva (https://www.bell-labs.com/
A complete description of the position is available at http://www.eurecom.fr/~
The PhD topic is at the intersection of machine learning and game theory, with application to modeling social networks. Candidates should have a strong background in mathematics (probability and preferably learning or game theory or both) and an interest in the application to social networks; and should hold a MSc degree (or equivalent) by the start of the PhD (expected Fall 2016).
Interested candidates are encouraged to apply before May 16, 2016, by sending the following documents to patrick[dot]loiseau[at]eurecom.fr and alonso[dot]silva[at]nokia.com: a detailed CV, a list of courses and grades in the last two years (at least), the name of 2-3 references willing to provide a recommendation letter for their application, a short statement of interest and any other information useful to evaluate the application.
Any question regarding the position can also be sent directly to the supervisors at patrick[dot]loiseau[at]eurecom.fr and alonso[dot]silva[at]nokia.com.
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