Wireless Sensor and Mobile Ad-hoc Networks (WiSeMAN) Research Lab
is looking for two highly self-motivated full-time Ph.D. students to conduct
cutting-edge research in the area of wireless sensor networks in the
Department of Computer and Information Science at the University of Michigan-Dearborn.


The two selected Ph.D. students will be hired to join WiSeMAN Research Lab starting
*September 1, 2014* and work on an NSF-funded project that is related to the design, analysis,
and implementation of sustainable and scalable three-dimensional wireless sensor networks
with a special focus on coverage, connectivity, localization, and geographic forwarding.


To be considered for this Ph.D. position, the interested candidate should have:
   -   Master's of Science in Computer Science, Computer Engineering, or Mathematics
   -   Solid knowledge in the area of networking and/or wireless sensor networks.
   -   Outstanding programming skills.
   -   Strong mathematics background.
   -   Excellent writing and communication skills in English.
   -   High GRE and TOEFL scores.


For full consideration, a *complete application* should be received by *June 15, 2014*,
and should include the following documents:
   -   Application letter.
   -   Detailed curriculum vitae, including a list of publications (if any).
   -   Copy of Master's thesis (if any).
   -   List of graduate courses taken along with grades.
   -   GRE and TOEFL scores


Please submit your *complete application* to Prof. Ammari at
hammari@umd.umich.edu.

PhD Student Position: Sparse Representation and Learning in Pattern Recognition
at Computer Science Department, Laval University, Canada

Keywords:  Sparse Representation, Structured Sparsity, Sparse Subspace Learning, Visual Recognition, Feature Selection, Sparse Coding, Sparsity Induced Similarity,

Background:  Sparse representation and learning have been extensively used recently in machine learning, computer vision, pattern recognition, etc. Generally speaking, sparse representation and learning aim to find the sparsest linear combination of basis functions from a complete dictionary. A rational behind this lies in the fact that there is a sparse connectivity between nodes in human brain.

In many signal processing applications (video, image processing, speech recognition, etc.)  the data sets are usually high dimensional and very large. In this context, sparse representation and learning have shown to be promising techniques for addressing them.

Recently, many important theoretical results enriched this area as for instance [1]: (i) the sparsest representation in a general dictionary is unique and can be found by using L1 minimisation [2]; (ii) the sparse representation can be covered by solving the convex programming, if the dictionary has a restricted isometry property [3,4]. Thanks to these important results and their corollaries, sparse representation and learning have extensively been used in many areas including signal processing and applications as speech recognition, machine learning, computer vision, digital multimedia, robotics, etc. A complete review on sparse representation and learning from both theory and applications sides appeared recently [1].

Goal and Objectives:

The goal of this PhD research is to strive to address the following issues:

• Is the sparsity assumption always supported by the data? Nowadays, compressive sensing has become one of the standard techniques of object recognition. If the sparsity is however not supported by the data, it is not guarantee to recover the exact signal and therefore sparse approximations may not deliver the robustness or performance desired [5]. In this case what sort of acceptable (in terms of computation load) robust method can be?   
• When the Sparse Representation is Relevant? It is important here to perform an in-depth analysis of sparse representation in pattern recognition and see empirically if this sparse representation improves recognition performance compared to non-sparse representations [6]. To this end, it would be important to take into consideration the way to extract features and to refine them as well as the computational load induced by the all process of sparse representation. 
• Sparse Representation or Collaborative Representation, which one is the best? Similarly to Zhang’s work [7], it would be appropriate to see if the use of all training samples to collaboratively represent a query sample is much more crucial to sparse representation based classification (SRC). Taking into consideration the fact that the collaborative representation based classification (CRC) plays a more important role than L1-regularization as shown by Zhang et al. [7], it would be opportune to see what new instantiations of CRC (with less computational load than usual SRC) can be proposed.
• Is sparse representation and learning usefulness in the context of video-based action modeling and recognition? Are ideas from this application fairly general and applicable to other recognition problems? One should here explore the usefulness of sparse representation and learning in the context of video classification, looking particularly at the problem of recognizing human actions-both physical actions and facial expressions [8]. This can be achieved by constructing an overcomplete dictionary using a set of spatio-temporal descriptors (extracted from the video sequences) in such a way that each of these descriptors is represented by some linear combination of a small number of dictionary elements. By doing so, one can achieve a more compact and richer representation than classical methods using clustering and vector quantization. It is also important to see which representation (sparse vs collaborative) is the more convenient for human-action recognition.  Experiments and validation of generalization to other recognition problems can be done on several data sets containing various physical actions, facial expressions and object recognition.

Job Description:

The PhD candidate will focus on signal processing and machine learning. In this context, she will first acquire expertise in different topics such as clustering and classification, Bayesian and generative modelling, signal separation, parameter and state estimation,  time series and space state methods, compression and coding. Then, the PhD candidate is expected to contribute to the advancement of the literature on sparse representation and learning along many different lines: methodological, theoretical, algorithmic and experimental.

Profile:

The applicant must have a Master of Science in Computer Science or Computer Engineering, Statistics, or related fields, possibly with background in Signal Processing and optimization. Good written and oral communication skills in English are required.

Application:

The application should include a brief description of research interests and past experience, a CV, degrees and grades, a copy of Master thesis (or a draft thereof), motivation letter (short but pertinent to this call), relevant publications (if any), and other relevant documents. Candidates are encouraged to provide letter(s) of recommendation and contact information to reference persons. Please send your application to chaib@ift.ulaval.ca. The deadline for the application is June15th, 2014, but we encourage the applicants to contact as soon as possible:

• Supervisors:  Pr. Brahim Chaib-draa
• Place: Department Computer Science and software Engineering, Laval University, Québec

Working Environment:

• The PhD candidate will work at Damas lab part of the Department Computer Science and software Engineering in collaboration with researchers from REPARTI center of which Chaib-draa is affiliated (http://reparti.gel.ulaval.ca/). REPARTI provides several advantages, including a human infrastructure to help with some management aspects for research, and access to a wide network of other researchers in areas closely related to the topic of this PhD proposal.

Benefits:

• Duration: 36 months – starting date: September 2014, 1^st
• Salary: 19 000$/Year + 3 000$/year (from University)

References:

1. Cheng, H.; Liu, Z.; Yang, L.; and Chen, X. Sparse Representation and Learning in Visual Recognition: Theory and Applications, Signal Processing, 93, 2013.
2. Donoho, D. and Elad, M. Optimally Sparse Representation in General (non-orthogonal) Dictionaries via L1 minimization. Proc. Of the National Academy of Sciences, 100(5), 2003.
3. Candes, E.J.; Romberg, J. K. and Tao, T. Stable Signal Recovery from Incomplete and Inaccurate Measurements. Communications on Pure and Applied Mathematics, 59(8), 2006.
4. Candes, E. J. and Tao, T. Near optimal Signal Recovery from random Projection: Universal Encoding Strategies? IEEE Transaction on Information Theory, 52(12), 2006.
5. Shi, Q,; Erikson, A.,; Hengel, A. and Shen, C. Is Face Recognition really a Compressive Sensing Problem? In Proc. of CVPR’11, 2011.
6. Rigamonti, R.; Brown, M. A. and Lepetit, V.  Are Sparse Representations Really Relevant for Image Classification? In Proc. of CVPR’11, 2011.
7. Zhang, L.; Yang, M.; and Feng X. Sparse Representation or Collaborative Representations: which Helps Face Recognition? IEEE Int. Conf on Computer Vision, 2011.
8. Guha, T. and Ward, R. K. Learning Sparse Representations for Human Action Recognition, IEEE Transaction on PAMI, 34(8), 2012.

 

The European Master's Program in Computational Logic

We are glad to announce to you the possibility to join our European Master's Program of Computational Logic. This program is offered jointly at the Free-University of Bozen-Bolzano in Italy, the Technische Universität Dresden in Germany, the Universidade Nova de Lisboa in Portugal and the Technische Universität Wien in Austria. Within this program you have the choice to study at two /three of the four European universities. In addition, you can do your project work at the National ICT of Australia (NICTA). You will graduate with a MSc in Computer Science and obtain a joint degree. Information on the universities and the program including the application procedure is provided here:

http://www.emcl-study.eu/home.html

Language of instruction is English. Tuition fees are 3.000 EUR (for non-European students) and 1.000 (for European students) per year.

A limited number of small scholarships is available.
(see: http://www.emcl-study.eu/fileadmin/emcl_booklet_tree/mss_jc_scholarship_scheme.html

).

If you have any further questions, do not hesitate to contact:

Prof. Dr. Steffen Hoelldobler
International Center for Computational Logic
Technische Universität Dresden
01062 Dresden, Germany

phone: [+49](351)46 33 83 40
fax: [+49](351)46 33 83 42
email: sh@iccl.tu-dresden.de

 

Position opened: PhD position in affective computing, Computer Science Department, University of Geneva, grant from the Swiss National Science Foundation.

 

Laboratories involved:

- Computer Vision and Multimedia Laboratory (http://cvml.unige.ch);

- French Literature Department (http://www.unige.ch/lettres/framo/index.html);

- Swiss Center for Affective Sciences (http://www.affective-sciences.org/);

 

Goal: to investigate how viewers react to those sequences in a movie that film critics and filmmakers consider as being emotionally relevant and therefore interesting in terms of aesthetic achievements.

 

Methods: multimedia processing, signal processing (experience in the analysis of physiological/behavioral signals and computer vision is particularly appreciated), statistical pattern recognition.

 

Profile: Master's degree in Computer Science, or electrical/computer engineering, or closely related field. Very strong background in signal/image/video processing and pattern recognition. Very good programming skills, taste and capacity to put new ideas into working prototypes. Very good written and oral communication skills in English.

 

Since this project will be conducted in very close cooperation with the Film Section at the Department of French Literature in the Faculty of the Humanities, a real plus in your application would be strong interest and achievements in film studies, or arts and humanities at large.

Although the working language is English, Geneva is a French-speaking place and experiments will be carried out with both French- and English-speaking participants; knowledge of French will be useful.

 

Starting date: as soon as possible.

 

Application: please include the following:

(1) a cover letter indicating motivation, research interests and expressing your interest in this particular, interdisciplinary area of research;

(2) a detailed curriculum vitae. Please, include, if available, list of publications (published or submitted);

(3) two letters of reference (the applicant should understand that the referees might be contacted).

 

Please collate all your files into one pdf and send your application by email to: Dr. Theodoros Kostoulas, Computer Vision & Multimedia Laboratory, Theodoros.Kostoulas@unige.ch.

 

Deadline for application: May 20th, 2014

 

 

Computer Vision and Multimedia Laboratory

Computer Science Department, University of Geneva, Battelle campus,

7, route de Drize, CH-1227 Carouge (Geneva), Switzerland

PHD STUDENTSHIP AT UCL IN LOGIC-BASED KNOWLEDGE REPRESENTATION AND REASONING

Duration of Studentship: 3 years full-time

Stipend: £15,863 + fees

The Department of Information Studies at University College London (UCLDIS) invites applications for a three year fully funded research studentship in the area of Knowledge Representation and Reasoning.

UCL, based in the heart of London, is consistently ranked as one of the World's top universities. It has a strong culture and tradition of interdisciplinary research, which is one of the factors in it currently attracting more research funding than any other UK university. UCLDIS is a multidisciplinary department, with research and teaching programmes across a broad spectrum of information-related subject areas such as library studies, archive studies, publishing, digital humanities, information management, computing and artificial intelligence. It has four distinct research groups, including the Knowledge Organization and Representation Group (KOARG,http://www.ucl.ac.uk/dis/research/koarg) which has a focus on the theoretical aspects and development of knowledge- and logic-based systems.

Studentship Description

The broad aim of the studentship will be to further research in the general area of logic-based knowledge representation and reasoning. The research will be co-supervised by Dr Rob Miller and Dr Antonis Bikakis, and so proposals for research that match with the particular interests of these individuals are particularly welcome. These interests include Reasoning about Actions, Commonsense Reasoning, Argumentation, Nonmonotonic Reasoning, Defeasible Reasoning, Logic Programming and Ambient Intelligence. Following normal UCL procedure, initial registration will be for the degree of MPhil with an expectation that the student will successfully upgrade to PhD registration in their second year. It is expected that the studentship will commence on 1 October 2014, although other start dates are possible. It will include full payment of UK/EU fees and an annual stipend as specified above.

Person Specification

Applicants should have a masters-level degree in an area related to computer science, mathematics or logic, an interest in the research areas mentioned above, and good written, communication, and organisational skills. A familiarity with logic-based knowledge representation and reasoning and/or knowledge-based systems would be an asset.

Eligibility and Application Procedure

Due to financial constraints on the level of fees included in the studentship, applications are restricted to candidates from the UK or other EU countries. General information for doctoral applicants is available on the DIS webpages at:http://www.ucl.ac.uk/dis/phd. There will be a two stage application process for this scholarship. In the first instance, Â please email a full CV, including the names and contact details of two referees, and a statement of proposed research (not exceeding 750 words) to Kerstin Michaels (k.michaels@ucl.ac.uk).

For further details of the studentship or an informal discussion please contact DrRob Miller (r.s.miller@ucl.ac.uk)) or Dr Antonis Bikakis (a.bikakis@ucl.ac.uk).


Closing Date: 23 May 2014

Latest time for the submission of applications: 17.00

Studentship Start Date: 1 October 2014

See link: www.ucl.ac.uk/dis/Research-studentships

Bakrie Graduate Fellowships

Selection Criteria

Applicants should have (a) a confirmed acceptance at the university partners (b) an excellent academic record with GPA of at least 3.5 for undergraduate and graduate level, (c) a good command of the English language, (d) assessed to have outstanding potential for leadership in government, business, or civil society after graduation.

Each Fellowship is tenable for one-year only for full-time students in master programs in almost all subjects.  Some professional based programs in medicine, business and law are excluded from the Bakrie Graduate Fellowships program.  The successful candidate is expected to complete his/her studies within the tenable period.

Selection Process

Candidates should send their application directly to the university partners.  Do not send the applications to the Bakrie Center Foundation.  The university will draw up a shortlist based on GPA scores. Those selected will then have to undergo a leadership assessment test arranged by the Bakrie Center Foundation.  The final score is based on weighted average of academic and leadership scores. The university will make the final announcement.

Each scholarship will cover the following:
    Tuition and other compulsory fees specified by the school.
    Monthly stipend
    Books
    Research Grant

PANDUAN BEASISWA BAKRIE GRADUATE FELLOWSHIP (BGF) Tahun Ajaran 2014 – 2015
Bakrie  Graduate Fellowship(BGF) atau Beasiswa Pascasarjana Bakrie adalah sebuah paket beasiswa yang disediakan oleh Bakrie Center Foundation (BCF) bagi mahasiswa pascasarjana.  Beasiswa ini diberikan kepada mahasiswa pascasarjana Program Magister tahun ke-2 untuk jangka waktu 12 bulan.
Calon mahasiswa penerima harus memenuhi beberapa persyaratan akademik dan kepemimpinan (leadership) melalui dua tahap seleksi. Seleksi Tahap I/Seleksi Potensi Akademik dilakukan oleh Program Pascasarjana. Seleksi Tahap II/Leadership Assessment dilaksanakan oleh BCF.

Seleksi Tahap I/Kompetensi Akademik
Seleksi Tahap I akan dilakukan oleh sebuah Tim Program Pascasarjana yang ditunjuk oleh Rektor.  Untuk keperluan ini, dalam usulannya  calon mahasiswa penerima diwajibkan untuk melampirkan:
1.      Ijazah S-1 dilegalisasi,
2.      Transkrip Akademik S-1 (IPK > 3,50) dilegalisasi,
3.      Transkrip Semester I S-2 (IPK > 3.50) dilegalisasi,
4.      Transkrip S2 sampai dengan Semester  2
5.      Indeks Prestasi Kumulatif (IPK) S1 minimum 3.50, tanpa nilai D dan IPK selama program S2
         minimum 3.50.
6.      Lolos assesment kriteria kepemimpinan yang akan dilakukan oleh Bakrie Center Foundation.
7.      Dalam penyusunan tugas akhir/tesis, mahasiswa penerima beasiswa wajib melakukan penelitian
         tugas akhir di WILAYAH RISET UNGGULAN yang menjadi prioritas bagi UNIVERSITAS
         seperti tersebut dalam Pasal 4.
8.      Penelitian yang dilakukan mahasiswa penerima beasiswa wajib dikerjasamakan dengan salah satu
         pusat penelitian di bawah koordinasi LPPM yang terdapat di instansi UNIVERSITAS.
9.      Mahasiswa penerima beasiswa wajib menyelesaikan program S2 nya dan dalam waktu paling lama
        12 bulan mengikuti Periode Tahun Akademik yang berlaku.
10.  Mahasiswa penerima beasiswa wajib menyerahkan Curricullum Vitae (CV)  yaitu di dalamnya meliputi
       kompetensi akademik dan lebih disukai yang memiliki pengalaman berorganisasi dalam bentuk
       pengurus organisasi, bekerja atau usaha sambil kuliah, menulis artikel di media cetak / elektronik atau
       publikasi ilmiah, prestasi akademik atau non akademik, dan menjadi relawan di lembaga yang
       terorganisir secara resmi seperti LSM, PMI, ORMAS, dll.
11.  Mahasiswa penerima beasiswa wajib menulis publikasi artikel minimum 600 kata, setiap semester baik
      itu mengenai hal yang berkaitan dengan penelitian tesisnya ataupun isu-isu strategis lainnya yang
      berkaitan dengan kegiatan program Bakrie Center Foundation (BCF). Publikasi dimaksud adalah di
      media cetak/ elektronik lokal (daerah). Sebagai penulis, agar juga disebutkan,
     “Penerima Bakrie Graduate Fellowship”.
12.  Dokumen Pengalamanorganisasi, bekerja, berusaha dan volunteer /Kegiatan yang pernah diikuti

Seleksi Tahap II/Penilaian Kemampuan Kepemimpinana
Salah satu keunggulan program beasiswa ini adalah menghasilkan kader dengan kemampuan akademik dan kepemimpinan mumpuni.  Oleh karena itu, selain asesmen kemampuan akademik pada Tahap I, BCF juga akan melakukan penilaianterhadap potensi kepemimpinan calon mahasiswa melalui wawancara.  Undangan wawancara akan dikirimkan oleh pihak BCF secara langsung kepada calon mahasiswa penerima melalui HP dan/atau e-mail. Mahasiswa penerima beasiswa dari sumber lain tidak diperkenankan untuk menerima BGF.

Pendaftaran
Pendaftaran dilakukan secara manual dengan mengisi Formulir Pendaftaran.  Formulir pendaftaran dapat diunduh dari website Program Pascasarjana. Pendaftaran yang telah dilengkapi dengan berkas-berkas yang diperlukan untuk Seleksi Tahap I dan Tahap II harap dikirimkan ke Direktur Program Pascasarjana.
Formulir  1 dan Formulir  2 diketik dengan rapi (font Calibri 10) soft file dikirim via email dan hardcopy dijilid dengan berkas yang lain 3 rangkap. 
Download Application form here

 

 

Jadwal Pelaksanaan Seleksi Beasiswa 

BAKRIE GRADUATE FELLOWSHIP(BGF)

Tahun Ajaran 2014-2015