Sabtu, 23 Juli 2016

Over 3000 PhD Positions in Italy

Over 3000 PhD Positions in Italy

62 PhD Positions at Scuola Superiore Sant'Anna, Pisa, Italy

87 PhD Positions at University of Trento, Italy
Deadline: July/August 2016

24 PhD Positions at University of Eastern Naples, Italy
Deadline: 29 July 2016

89 PhD Positions at University of Messina, Italy
Deadline: 30 July 2016

813 PhD Positions at University of Rome 1 "La Sapienza", Italy
Deadline: 1 August 2016

62 PhD Positions at University of Chieti-Pescara, Italy
Deadline: 3 August 2016

106 PhD Positions at University of Ferrara, Italy
Deadline: 5 August 2016

77 PhD Scholarships at University of Udine, Italy
(Full information in Italian version)
Deadline: 8 August 2016

33 PhD Positions at University of Eastern Piedmont, Italy
Deadline: 8 August 2016

50 PhD Positions at University of Teramo, Italy
Deadline: 8 August 2016

233 PhD Positions at University of Florence, Italy
Deadline: 10 August 2016

48 PhD Scholarships at University of Naples "Parthenope", Italy
Deadline: 22 August 2016

69 PhD Positions at University of Insubria, Italy
Deadline: 22 August 2016

32 PhD Positions at University of Macerata, Italy
Deadline: 23 August 2016

103 PhD Positions at University of Siena, Italy
Deadline: 24 August 2016

60 PhD Positions at University of L'Aquila, Italy
Deadline: 26 August 2016

30 PhD Positions at University of Cassino, Italy
Deadline: 26 August 2016

58 PhD Positions at University of Brescia, Italy
Deadline: 30 August 2016

124 Positions at University of Bari, Italy
Deadline: 31 August 2016

41 PhD Scholarships from Gran Sasso Science Institute, Italy
Deadline: 1 September 2016

32 PhD Positions at University of Urbino, Italy
Deadline: 1 September 2016

39 PhD Positions at University of Camerino, Italy
Deadline: 2 September 2016

148 PhD Positions at University of Parma, Italy
Deadline: 5 September 2016

171 PhD Positions at Sacred Heart Catholic University, Italy
Milan, Piacenza, and Brescia Campus
Rome Campus
Deadline: 5 September 2016

107 PhD Positions at University of Catania, Italy
Deadline: 6 September 2016

128 PhD Positions at University of Salerno, Italy
Deadline: 8 September 2016

295 PhD Positions at University of Naples "Federico II", Italy
Deadline: 8 September 2016

34 PhD Positions at University of Molise, Italy
Deadline: 16 September 2016

Rabu, 22 Juni 2016

FULL SCHOLARSHIP di TAIWAN

Untuk yang mau mencari beasiswa full (Mengcover uang kuliah dan biaya hidup) untuk S-1/ S-2/ S-3 di Taiwan, silahkan datang pada:

Sabtu, 6 Agustus 2016, 15.00 - 20.00 di Hotel Majapahit (SURABAYA)
Minggu, 7 Agustus 2016, 15.00 - 20.00 di Hotel Santika Premiere (YOGYAKARTA)

Diikuti oleh TEC Indonesia dan tiga universitas terbaik di Taiwan:
1. NTU (The best comprehensive university in Taiwan)
2. NTUST (The best technological university in Taiwan)
3. NTNU (The best educational university in Taiwan)
4. Taiwan Education Center Indonesia (Official information center of Study in Taiwan, representing all universities)

Keterangan:
1. Terbuka untuk umum/ GRATIS.
2. Tersedia beasiswa full untuk S-1/ S-2/ S-3 SEMUA JURUSAN.
3. Program FULL BAHASA INGGRIS (Tidak perlu bisa bahasa Mandarin sama sekali).
4. Tidak ada KONTRAK atau ikatan kerja setelah lulus.
5. Bisa langsung MENDAFTAR dan INTERVIEW di tempat, silahkan bawa semua berkas pendaftaran.

Info detail bisa langsung add LINE saya, ID: ivan_arista

All the best.
 
Best Regards,
 
Ivan Arista S., MBA.

Mobile: +62 (0) 81 2310 0679
LINE ID: ivan_arista

Jumat, 17 Juni 2016

Over 1300 PhD Scholarships in Italy

Over 1300 PhD Scholarships in Italy

62 PhD Positions at Scuola Superiore Sant'Anna, Pisa, Italy

49 PhD Scholarships at Free University of Bozen-Bolzano, Italy
Deadline: 8 June 2016

24 PhD Scholarships at University of Bergamo, Italy
Deadline: 9 June 2016

292 PhD Scholarships at University of Genoa, Italy
Deadline: 10 June 2016

91 PhD Positions at University of Trieste, Italy
Deadline: 16 June 2016

415 PhD Positions at University of Padua, Italy
Deadline: 17 June 2016

156 PhD Positions at University of Milano-Bicocca, Italy
Deadline: 22 June 2016

PhD Scholarships at University of Trento, Italy
Deadline: 12 July 2016

34 PhD Scholarships at IMT School for Advanced Studies, Lucca, Italy
Deadline: 13 July 2016

77 PhD Scholarships at University of Udine, Italy
(Full information in Italian version)
Deadline: 8 August 2016

48 PhD Scholarships at University of Naples "Parthenope", Italy
Deadline: 22 August 2016

41 PhD Scholarships from Gran Sasso Science Institute, Italy

Deadline: 1 September 2016

Kamis, 02 Juni 2016

Postdoctoral fellowship at University of Quebec (Montreal, Canada)

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

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.com/en/what:job/jobID:100048/

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/pmoap>www.uni-oldenburg.de/pmoap.

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/stellen/?stelle=64797

--
---
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/postlaurea/dottorati/XXXII/ENG/. Application deadline: June 10, 2016 at 12.00 noon (Italian time).
—————————————————–
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)

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