- Comp-neuro - lists.cnsorg.org

3 PhD studentships in London
by Alonso, Eduardo March 16, 2016

March 16, 2016

Dear all, City University London is offering 3 fully-funded PhD studentships. Doctoral studentships will provide, for 3 years, an annual bursary of £16,000. They also include full tuition fees. The main focus is the development of computational models of learning and behaviour, with a particular interest in the application of Deep Learning architectures and algorithms. In addition, we want to explore how the resulting models could inform research in learning impairments associated with neurodevelopment disorders. Submission deadline is the 26th of May, with successful applicants starting in Oct 2016. If you are interested, please contact me. Dr Eduardo Alonso Reader in Computing Deputy Head of Department Department of Computer Science School of Mathematics, Computer Science and Engineering City University London London EC1V 0HB United Kingdom T: +44 (0)20 7040 4049 E: E.Alonso(a)city.ac.uk http://www.city.ac.uk/people/academics/eduardo-alonso

1 0

A funding opportunity for Assistive Technology for ALS patients
by Neta Zach March 15, 2016

March 15, 2016

The ALS Association and Prize4Life's are happy to anounce the launch of a new program to promote ALS technology development- *the ALS Assistive Technology Challenge* <http://www.alsa.org/research/als-assistive-technology-challenge-2.html>*- *a program focused around accelerating the development of communication technologies for locked in patients with development funds of over $500,000. *In the challenge, open until July 29, teams can participate in a $400,000 challenge to develop a prototype that will enable communication for ALS patients. The top finalists will be invited to present their prototype in December 2016 in Dublin where the award winner will be selected. *You can read about it more *here <http://www.prnewswire.com/news-releases/the-als-association-and-prize4life-…>.*

1 0

Faculty Position in Neural Data and Imaging Sciences, Sabanci Univ., Istanbul
by Mujdat Cetin March 15, 2016

March 15, 2016

*Faculty Position in Neural Data and Imaging Sciences at Sabancı University* http://ee.sabanciuniv.edu/en/full-time-faculty-position-neural-data-and-ima… The Faculty of Engineering and Natural Sciences (FENS) at Sabancı University, İstanbul, Turkey invites applications for a full-time faculty position in the area of Neural Data and Imaging Sciences with a targeted start date of Fall 2016. The ideal candidate for this position would have a Ph.D. degree in electrical engineering, computer science, biomedical engineering, computational neuroscience, or a related field, as well as a strong research and publication record in the area of information extraction from challenging neural data. Particular research areas of interest include but are not limited to neuroinformatics, big neural sensor data analytics, biomedical imaging, brain informatics, signal processing of neural activity, computational neuroimaging, neural image analysis, machine learning for understanding the brain, and connectomics. Sensing/imaging modalities of interest include but are not necessarily limited to MRI, fMRI, DTI, PET, EEG, MEG, ECoG, and several forms of microscopy. Application areas of interest include, but are not limited to healthcare, neurotechnology, brain-computer interfaces, neurorehabilitation, and neurorobotics. The faculty member will have a primary affiliation with the Electronics Engineering program but will be expected to conduct collaborative research with other programs in FENS as well. Applications from candidates at all ranks are encouraged. For candidates at the Assistant Professor level, post-doctoral research experience is a plus. Successful candidates are expected to develop externally-funded internationally-recognized research programs; teach undergraduate and graduate courses; advise and mentor graduate and undergraduate students; and undertake service responsibilities. Successful candidates will have the opportunity to benefit from the state-of-the-art infrastructure of the brand-new Nanotechnology Research and Application Center (SUNUM), as well as to be involved in the Professional Master’s Program in Data Analytics. Sabancı University, a privately funded premier academic institution, established in 1996 in its state-of-the-art campus located in suburban İstanbul. The university offers 13 undergraduate programs and 38 graduate programs in three faculties (Faculty of Engineering and Natural Sciences, Faculty of Arts and Social Sciences, School of Management). The University aims to establish a high standard of academic excellence and a research-oriented atmosphere for its international faculty and students, while nurturing productive relationships particularly with the industries in the region which constitute the industrial heartland of Turkey. The medium of instruction is English. In 2015, Sabancı University ranked 13th globally in the Times Higher Education (THE) ranking of the top 100 universities under 50 years old, and 21st in the THE Asia university rankings. In the recently announced THE ranking of the world’s best small universities in 2016, Sabancı University ranked 10th globally. It ranked first among all universities in Turkey in the University Entrepreneurship and Innovation Index of the Ministry of Science, Industry, and Technology in 2015. Detailed information about the Electronics Engineering Program at Sabancı University can be found at http://ee.sabanciuniv.edu/ Applicants should submit their applications online through https://crm.sabanciuniv.edu/acadcv/application.php and provide a cover letter, curriculum vitae, statement of research interests, statement of teaching interests, and at least three professional references. Review of applications will begin on May 1, 2016 and will continue until the position is filled. -- Mujdat Cetin Technical Program Co-chair, IVMSP 2016 -- http://ivmsp2016.org Technical Co-chair, FUSION 2016 -- http://fusion2016.org Faculty Member Faculty of Engineering and Natural Sciences Sabanci University Orhanli, Tuzla 34956, Istanbul, Turkey Phone : +90 (216) 483-9594 Fax : +90 (216) 483-9550 E-mail : mcetin(a)sabanciuniv.edu Web : http://spis.sabanciuniv.edu

1 0

Fully funded postdoctoral position in Machine Learning and Computational Biology at Royal Holloway University of London
by Alberto Paccanaro March 15, 2016

March 15, 2016

[Apologies for cross-posting] Application deadline: 10th April 2016 ==================================================== Applications are invited for a postdoctoral position at the Department of Computer Science of Royal Holloway, University of London. The successful candidate will work with Prof Alberto Paccanaro on different projects in the area of Systems Biology and Network Medicine -- please see http://www.paccanarolab.org/ for a description of the research carried out in the lab. Candidates should have a PhD degree in a relevant quantitative field (e.g. Computer Science, Statistics, Engineering), a strong background in machine learning and a keen interest in computational biology. Previous experience in the area of graphical models would be an asset for this position. The successful candidate will have the opportunity to collaborate with members of the interdisciplinary Centre for Systems and Synthetic Biology (Computer Science and Biological Sciences) at Royal Holloway and collaborators at Imperial College, University of Tennessee and Yale University. The post is therefore ideal for someone with a computer science or maths background who is looking to move into computational biology. This post is based in the Department of Computer Science in Egham, Surrey where the College is situated in a beautiful, leafy campus near to Windsor Great Park and within commuting distance from London (see http://www.royalholloway.ac.uk/) This is a full time post, available immediately, or as soon as possible thereafter for a fixed term period of 30 months. The salary is in the range £33,789 to £39,902 per annum inclusive of London Allowance. The position is funded by a joint BBSRC/NSF grant. To apply go to: https://jobs.royalholloway.ac.uk/vacancy.aspx?ref=0316-094 For an informal discussion about the post, please contact Prof Alberto Paccanaro, alberto(a)cs.rhul.ac.uk. The Human Resources Department can be contacted with queries by email at: recruitment(a)rhul.ac.uk. Please quote the reference: 0316-094. Closing Date: Midnight, 10th April 2016. Interview Date: To be confirmed. The College is committed to equality and diversity, and encourages applications from all sections of the community. -- ===================================== Prof. Alberto Paccanaro Department of Computer Science Royal Holloway, University of London Phone: +44 1784 414239 Homepage: http://www.cs.rhul.ac.uk/home/alberto/ Lab page: http://www.paccanarolab.org

1 0

ICLR 2016 Registration Open
by Hugo Larochelle March 15, 2016

March 15, 2016

4th International Conference on Learning Representations (ICLR 2016) Website: http://www.iclr.cc/ Location: Caribe Hilton, San Juan, Puerto Rico, May 2-4, 2016 Registration to ICLR 2016 is now open: http://www.iclr.cc/doku.php?id=iclr2016:registration The lower early registration fees and hotel rates are available until April 1st. For more information on ICLR 2016, please visit http://www.iclr.cc/doku.php?id=iclr2016:main. We look forward to seeing you in Puerto Rico! Yoshua Bengio, Université de Montreal, General Chair Yann LeCun, New York University and Facebook, General Chair Hugo Larochelle, Twitter and Université de Sherbrooke, Senior Program Chair Brian Kingsbury, IBM Watson Group, Program Chair Samy Bengio, Google, Program Chair

1 0

Funded vision science PhD opportunities at Royal Holloway, University of London
by Durant, Szonya March 15, 2016

March 15, 2016

Please see opportunities below to work with me (Szonya Durant) as supervisor and Angelika Lingnau or Jonas Larsson in the Department of Psychology at Royal Holloway, University of London. There are two main proposed areas: Investigating the neural representation of action recognition categories What are the neural sources and function of expectation signals in human visual processing? Both projects involve EEG and fMRI methods, but experience is not essential and training will be provided as part of the PhD. Please email szonya.durant(a)rhul.ac.uk<mailto:szonya.durant@rhul.ac.uk> for more detail on these projects and the application process. Strong students from Psychology/Neuroscience/Cognitive Science/Computer Science/Biological Science/Physics/Mathematics are encouraged to apply. Suitable candidates (in collaboration with the supervisor) will need to develop a PhD proposal, which the department will use to evaluate applications - the department deadline for proposals is the 15th of April (so please get in touch asap). There are 4 paid studentships available in the department for UK and EU students. ********************************************************************** Szonya Durant Department of Psychology Royal Holloway, University of London Egham TW20 0EX 01784 276522

1 0

A new julia package for modelling spiking neural network
by Yao Lu March 15, 2016

March 15, 2016

SpikingNetworks.jl <https://github.com/YaoLuCNS/SpikingNetworks.jl> is a julia package for computational neuroscientists to simulate large scale spiking neural network. It is inspired by Brain2 <http://briansimulator.org/>, and achieve most of the functionalities in Brian2 with faster speed, cleaner syntax and flexibility. Julia <http://julialang.org/> is a technical computing language, which relies on LLVM to achieve efficiency comparable to C. Julia has MATLAB-like syntax and is user-friendly to MATLAB users. This this package aims at experienced computational neuroscientists who want faster speed. Yao Lu

1 0

PhD studentships at Keele University - Closing Date: 15 April 2016
by Peter Andras March 14, 2016

March 14, 2016

*Fully Funded Computer Science PhD Studentships at Keele University* The Faculty of Natural Sciences at Keele University is pleased to announce up to 7 fully-funded PhD studentships for UK/EU students for start in September/October 2016, as well as opportunities for part-funded PhD studentships for suitable candidates. Applications are invited from high quality prospective doctoral candidates in Computer Science and other disciplines. We welcome candidates with a good fit to our research specialisms and the expertise of our academic staff. Candidates are expected to have a 1st class or 2.1 class undergraduate degree or a Distinction level MSc degree in a relevant subject (or equivalent in the case of candidates from other EU countries). One of our focus areas in Computer Science is *Computational Neuroscience and Biomedical Engineering*. A selection of topics of interests in this area is provided below. *Computational Neuroscience and Biomedical Engineering*: computational modelling of the stomatogastric nervous system; bio-inspired robot control; computational modelling of cognitive behaviour in bees; neuro-control of evolving artificial creatures; neuro-control of robotic swarms; computational analysis of neuroimaging and multi-electrode array data; human and animal behaviour analysis using accelerometer data; virtual reality and robotics for rehabilitation engineering; analysis and modelling of the impact of neuromodulators in biological neural systems; big data analytics and neuro-degenerative disorders. *Funding* Each fully-funded studentship includes an annual tax-free stipend at RCUK rate plus tuition fees (currently £14,296 for stipend and £4,121 for fees). The funding is available for 3 years. Each studentship has an associated research expense budget (£2,000 – £3,000 per year, depending on the nature of the project, to cover project related consumables and conference travel). Opportunities are also available for candidates from the UK/EU and overseas who can demonstrate that they can fully or partially self-fund. If you are interested in this route, please contact Professor Peter Andras at p.andras(a)keele.ac.uk for a discussion. *Closing Date* The *closing date* for applications is Friday *15 April 2016* Full details and application procedures can be found on the website: http://www.keele.ac.uk/pgresearch/howtoapply/ For the purpose of research proposal applicants should submit an outline of their research interests or of a particular research project that the applicant would like to work on (max. 2 pages). Please send any questions by email to Lisa Cartlidge ( l.j.cartlidge(a)keele.ac.uk) or Ann Billington (a.billington(a)keele.ac.uk)

1 0

Postdoctoral Position in Computational Neuroscience/Biophysical Modeling
by Gabbiani, Fabrizio March 14, 2016

March 14, 2016

Postdoctoral Position in Computational Neuroscience/Biophysical Modeling Computational and Applied Mathematics Rice University and Department of Neuroscience Baylor College of Medicine Houston, Texas ----- A postdoctoral position in computational neuroscience is available at Rice University and Baylor College of Medicine, in the laboratories of Steven Cox (Rice University) and Fabrizio Gabbiani (Baylor College of Medicine). Our research focuses on understanding the biophysical mechanisms underlying the implementation of non-linear operations by neurons and neuronal circuits. The postdoctoral research associate will model the cellular and network mechanisms underlying collision avoidance behaviors using advanced mathematical techniques and computer simulations. Modeling will be supported by a large data set of experimental data gathered using electrophysiology, pharmacology, calcium imaging, high-speed video imaging and telemetry. We are looking for a highly motivated candidate with a strong background in computational modeling of single neurons and neuronal circuits. Experience with Matlab and NEURON is required. The position is available for one year, with possibility of renewal for a second year, contingent of performance and funding availability. Salary will be commensurate with level of experience, based on an NIH scale. Applications will be accepted until April 15, 2016. Our labs are located at Rice University and in the adjacent Texas Medical Center, close to many of Houston's cultural and outdoor amenities. For further information about Rice University, the Texas Medical Center and Houston please visit http://www.explore.rice.edu/explore/General_Information.asp or https://www.bcm.edu/about-us/life-in-houston For further informal inquiries and to apply, please send CV, the names and full contact information of two to three references, as well as one to two representative publications to cox(a)rice.edu and gabbiani(a)bcm.edu. Equal Opportunity Employer: Females / Minorities / Veterans/ Disabled / Sexual Orientation / Gender Identity.

1 0

Eleventh Advanced Statistics and Data Mining Summer School (Madrid, June 27th - July 8th, 2016)
by asdm＠fi.upm.es March 14, 2016

March 14, 2016

Dear colleagues, The Technical University of Madrid (UPM) will once more organize the 'Madrid UPM Advanced Statistics and Data Mining' summer school. The summer school will be held in Boadilla del Monte, near Madrid, from June 27th to July 8th. This year's edition comprises 12 week-long courses (15 lecture hours each), given during two weeks (six courses each week). Attendees may register in each course independently. No restrictions, besides those imposed by timetables, apply on the number or choice of courses. Early registration is now *OPEN*. Extended information on course programmes, price, venue, accommodation and transport is available at the school's website: http://www.dia.fi.upm.es/ASDM There is a 25% discount for members of Spanish AEPIA and SEIO societies. Please, forward this information to your colleagues, students, and whoever you think may find it interesting. Best regards, Pedro Larranaga, Concha Bielza, Bojan Mihaljevic and Laura Anton-Sanchez. -- School coordinators. *** List of courses and brief description *** * Week 1 (June 27th - July 1st, 2016) * 1st session: 9:45-12:45 Course 1: Bayesian Networks (15 h) Basics of Bayesian networks. Inference in Bayesian networks. Learning Bayesian networks from data. Real applications. Practical demonstration: GeNIe, Weka, Bayesia, R. Course 2: Time Series(15 h) Basic concepts in time series. Descriptive methods for time series. Linear models for time series. Extensions. Practical demonstration: R. 2nd session: 13:45-16:45 Course 3: Supervised Pattern Recognition (15 h) Introduction. Assessing the performance of supervised classification algorithms. Preprocessing. Classification techniques. Combining multiple classifiers. Comparing supervised classification algorithms. Practical demonstration: Weka. Course 4: Bayesian Inference (15 h) Introduction: Bayesian basics. Conjugate models. MCMC and other simulation methods. Regression and Hierarchical models. Model selection. Practical demonstration: R and WinBugs. 3rd session: 17:00 - 20:00 Course 5: Neural Networks and Deep Learning (15 h) Introduction. Training algorithms. Learning and Optimization. MLPs in practice. Deep Networks. Course 6: Unsupervised Pattern Recognition (15 h) Introduction to clustering. Data exploration and preparation. Prototype-based clustering. Density-based clustering. Graph-based clustering. Cluster evaluation. Miscellanea. Conclusions and final advise. Practical session: R. * Week 2 (July 4th - July 8th, 2016) * 1st session: 9:45-12:45 Course 7: Statistical Inference (15 h) Introduction. Some basic statistical test. Multiple testing. Introduction to bootstrap methods. Introduction to Robust Statistics. Practical demonstration: R. Course 8: Big Data with Apache Spark (15 h) Introduction. Spark framework and APIs. Data processing with Spark. Spark streaming. Machine learning with Spark MLlib. 2nd session: 13:45-16:45 Course 9: Text Mining (15 h) Introduction. Language Modeling. Text Similarity. Text Classification. Information Extraction. Practical session: Python, with Jupyter notebooks. Course 10: Feature Subset Selection (15 h) Introduction. Filter approaches. Embedded methods. Wrapper methods. Advanced topics. Practical session: R and Weka. 3rd session: 17:00-20:00 Course 11: Support Vector Machines and Regularized Learning (15 h) Introduction. SVM models. SVM learning algorithms. Convex non differentiable optimization. Course 12: Hidden Markov Models (15 h) Introduction. Discrete Hidden Markov Models. Basic algorithms for Hidden Markov Models. Semicontinuous Hidden Markov Models. Continuous Hidden Markov Models. Unit selection and clustering. Speaker and Environment Adaptation for HMMs. Other applications of HMMs. Practical session: HTK.

1 0