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postdoctoral position, Deep Brain Stimulation in Parkinson's disease, IUPUI and Purdue University
by Leonid Rubchinsky Aug. 12, 2014

Aug. 12, 2014

Postdoctoral opening in computational neuroscience The Department of Mathematical Sciences at Indiana University-Purdue University Indianapolis (IUPUI) invites applications for a postdoctoral position in the area of mathematical and computational neuroscience. The position is with a collaborative group of scholars from the Department of Mathematical Sciences at IUPUI, Weldon School of Biomedical Engineering at Purdue University and the Department of Neurosurgery of the Indiana University School of Medicine. The current research project is aimed at the development of adaptive deep brain stimulators for Parkinson's disease. Part of the duties may also include some teaching in the Mathematical Sciences. The position offers excellent interdisciplinary training possibilities in mathematical biology and computational neuroscience. Qualifications: Applicants are expected to have a Ph.D. in mathematics, physics, neuroscience, biomedical engineering, computer science or other related field. Applicants should have strong quantitative skills in data analysis and modeling and a strong interest in neuroscience applications. Experience in nonlinear dynamics/control theory/neurophysiology is a plus. How to apply: Send your CV with a list of publications, research statement, and arrange two-three recommendation letters to be sent to Dr. Leonid Rubchinsky via e-mail: leo(a)math.iupui.edu. Address all your inquiries to the same e-mail. Alternatively send your application by regular mail to: Dr. Leonid Rubchinsky, Department of Mathematical Sciences, IUPUI, 402 N. Blackford Street, LD 270, Indianapolis, IN 46202. Screening of applications will continue until position is filled. IUPUI is an EEO/AA Employer, M/F/D. *********************** Leonid Rubchinsky, PhD Associate Professor Department of Mathematical Sciences, Indiana University - Purdue University, Indianapolis Stark Neurosciences Research Institute, Indiana University School of Medicine 402 N. Blackford St Indianapolis, IN 46202 leo(a)math.iupui.edu http://www.math.iupui.edu/~leo 317-274-9745 317-274-3460 (fax) ***********************

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Job offer: PhD student position
by Seeger, Simone Aug. 12, 2014

Aug. 12, 2014

The Department of Theoretical Neuroscience (Head: Prof. Daniel Durstewitz) at the Central Institute of Mental Health (Mannheim) invites applications for a PhD student position (E 13 TV-L) (50% of the fulltime weekly hours) in the newly founded research group Data-driven network models of higher cognitive functions (Head: Dr. Joachim Hass) to be filled as soon as possible. The position is initially limited for 3 years. The research group develops biologically realistic computational neural network models of the neocortical structures such as the prefrontal cortex and the motor cortex. These models closely adhere to physiological data from both in vitro and in vivo experiments and are used to study higher cognitive functions such as time perception and working memory. The successful candidate will work in the project "The human mirror neuron system - measurement and beyond" funded by the Heidelberg Academy for Sciences and Humanities. The joint project of the Departments of Theoretical Neuroscience and Clinical Psychology of the Central Institute of Mental Health aims at a deeper understanding of the mirror neuron system in humans, which is thought to play a crucial role in social cognition by representing the emotions and intentions of others in the motor cortex. The project combines multimodal measurements (involving fMRI, EEG, transcranial magnetic stimulation and genotyping) with computational modeling. The Central Institute of Mental Health is an internationally renowned research institute for psychiatry and neuroscience as well as a clinic for psychiatry, psychotherapy and psychosomatics (part of the medical faculty of the University of Heidelberg). The research group closely interacts with neurobiologists and psychologists at the institute. The Department of Theoretical Neuroscience is focused on computational modeling and statistical data analysis of prefrontal cortex and hippocampus functions. It is one of the core research units of the Bernstein Center for Computational Neuroscience (BCCN) Heidelberg-Mannheim. Access to high-performance computing facilities is provided. Tasks: The project involves the development of a rate-based, brain-scale model as well as the further development of an existing spiking network model of the motor cortex. The resulting twostage model is then being adapted to the multimodal experimental data using stochastic optimization techniques. In particular, the effects of the transcranial magnetic stimulation and different levels of dopamine and oxytocin are to be implemented in the model and directly compared with the experimental data. Participation in the project organization and publication writing is also expected. It is also possible to participate in the analysis of the fMRI data. Requirements: The candidate should have a university degree (master or equivalent) in physics, mathematics, computer science, computational neuroscience or similar, a strong interest in neurobiological research and very good programming skills (ideally in MATLAB and C). Good communication skills in English as well as knowledge in nonlinear dynamics, numerical optimization or neural modeling are also required. Knowledge in neuroscience or experimental psychology is beneficial. Applicants should sent their application documents (cover letter including a brief description of personal qualifications and future research interests, CV and contact details of two personal references) to joachim.hass(a)zi-mannheim.de<mailto:joachim.hass@zi-mannheim.de>. Questions and informal discussions about the position are also welcome under this email address. The call is open until the position is filled. *** Simone Seeger, M.A. Administration Bernstein Center for Computational Neuroscience Zentralinstitut für Seelische Gesundheit Postfach 12 21 20, 68072 Mannheim J5, 68159 Mannheim Telefon: 0621/1703-1326 oder 06221/54-8310 Fax: 0621/1703-2915 E-Mail: Simone.Seeger(a)zi-mannheim.de<mailto:Simone.Seeger@zi-mannheim.de> Internet: http://www.bccn-heidelberg-mannheim.de<http://www.bccn-heidelberg-mannheim.de/> *** Simone Seeger, M.A. Administration Bernstein Center for Computational Neuroscience Zentralinstitut für Seelische Gesundheit Postfach 12 21 20, 68072 Mannheim J5, 68159 Mannheim Telefon: 0621/1703-1326 oder 06221/54-8310 Fax: 0621/1703-2915 E-Mail: Simone.Seeger(a)zi-mannheim.de<mailto:Simone.Seeger@zi-mannheim.de> Internet: http://www.bccn-heidelberg-mannheim.de<http://www.bccn-heidelberg-mannheim.de/>

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Call for Papers: IEEE Computational Intelligence Magazine (CIM) Special Issue: "Computational Intelligence for Changing Environments"
by Dr Amir Hussain Aug. 11, 2014

Aug. 11, 2014

Call for Papers (Please also forward to any interested colleagues - with advance apologies for any cross-postings!) IEEE Computational Intelligence Magazine (CIM) (http://cis.ieee.org/ieee-computational-intelligence-magazine.html) Special Issue on "Computational Intelligence for Changing Environments" (Submission Deadline: 15 Nov 2014) (http://www.cs.stir.ac.uk/~ahu/IEEE-CIM-CICE2015.pdf) Guest Editors: Amir Hussain, Dacheng Tao, Jonathan Wu and Dongbin Zhao Aims and Scope: Over the past decade or so, computational intelligence techniques have been highly successful for solving big data challenges in changing environments. In particular, there has been growing interest in so called biologically inspired learning (BIL), which refers to a wide range of learning techniques, motivated by biology, that try to mimic specific biological functions or behaviors. Examples include the hierarchy of the brain neocortex and neural circuits, which have resulted in biologically-inspired features for encoding, deep neural networks for classification, and spiking neural networks for general modelling. To ensure these models are generalizable to unseen data, it is common to assume that the training and test data are independently sampled from an identical distribution, known as the sample i.i.d. assumption. In dynamic and non-stationary environments, the distribution of data changes over time, resulting in the phenomenon of ‘concept drift’ (also known as population drift or concept shift), which is a generalization of covariance shift in statistics. Over the last five years, transfer learning and multitask learning have been used to tackle this problem. Fundamental analyses using probably approximately correct (PAC) and Rademacher complexity frameworks have explained why appropriate incorporation of context and concept drift can improve generalizability in changing environments. It is possible to use human-level processing power to tackle concept drift in changing environments. Concept drift is a real-world problem, usually associated with online and concept learning, where the relationships between input data and target variables dynamically change over time. Traditional learning schemes do not adequately address this issue, either because they are offline or because they avoid dynamic learning. However, BIL seems to possess properties that would be helpful for solving concept drift problems in changing environments. Intuitively, the human capacity to deal with concept drift is innate to cognitive processes, and the learning problems susceptible to concept drift seem to share some of the dynamic demands placed on plastic neural areas in the brain. Using improved biological models in neural networks can provide insight into cognitive computational phenomena. However, a main outstanding issue in using computational intelligence for changing environments and domain adaptation is how to build complex networks, or how networks should be connected to the features, samples, and distribution drifts. Manual design and building of these networks are beyond current human capabilities. Recently, computational intelligence methods has been used to address concept drift in changing environments, with promising results. A Hebbian learning model has been used to handle random, as well as correlated, concept drift. Neural networks have been used for concept drift detection, and the influence of latent variables on concept drift in a neural network has been studied. In another study, a timing-dependent synapse model has been applied to concept drift. These works mainly apply biologically-plausible computational models to concept drift problems. Although these results are still in their infancy, they open up new possibilities to achieve brain-like intelligence for solving concept drift problems in changing environments. Taking the current state of research in computational intelligence for changing environments into account, the objective of this special issue is to collate this research to help unify the concepts and terminology of computational intelligence in changing environments, and to survey state-of-the-art computational intelligence methodologies and the key techniques investigated to date. Therefore, this special issue invites submissions on the most recent developments in computational intelligence for changing environments algorithms and architectures, theoretical foundations, and representations, & their application to real world problems. We also welcome timely surveys & review papers. Topics of Interest include (but are not limited to): • Computational intelligence methodologies and implementation for changing environments • Transfer learning, Multitask learning, Domain adaption • Incremental Learning architectures, Unsupervised and semi-supervised learning architectures • Incremental Knowledge augmentation, Representation learning and disentangling • Incremental Adaptive Neuro-fuzzy systems • Incremental and single-pass data mining • Incremental Neural Clustering & Regression • Incremental Adaptive decision systems • Incremental Feature selection and reduction • Incremental Constructive Learning • Novelty detection in Incremental learning Submission Process The maximum length for the manuscript is typically 25 pages in single column format with double-spacing, including figures and references. Authors should specify in the first page of their manuscripts the corresponding author’s contact and up to 5 keywords. Submission should be made via: https://easychair.org/conferences/?conf=ieee-cim-cice2015 Important Dates (for August 2015 Issue) 15th November, 2014: Submission of Manuscripts 15th January, 2015: Notification of Review Results 15th February, 2015: Submission of Revised Manuscripts 15th March, 2015: Submission of Final Manuscripts Guest Editors Professor Amir Hussain, University of Stirling, Stirling FK9 4LA, Scotland UK Email: ahu(a)cs.stir.ac.uk http://cs.stir.ac.uk/~ahu/ Professor Dacheng Tao, University of Technology, Sydney, 235 Jones Street, Ultimo, NSW 2007, Australia Email: dacheng.tao(a)uts.edu.au Professor Jonathan Wu University of Windsor 401 Sunset Avenue, Windsor, ON, Canada Email: jwu(a)uwindsor.ca Professor Dongbin Zhao Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China E-mail: dongbin.zhao(a)gmail.com -- The University of Stirling has been ranked in the top 12 of UK universities for graduate employment*. 94% of our 2012 graduates were in work and/or further study within six months of graduation. *The Telegraph The University of Stirling is a charity registered in Scotland, number SC 011159.

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FW: PhD/Researcher positions in Electrical Engineering, Computer Science or Biomedical Engineering
by Morgado Dias Aug. 9, 2014

Aug. 9, 2014

Dear Members, Positions 3 and 4 might be interesting for the community. Best regards, Morgado ------------------------------------------------------ PhD/Researcher positions in Electrical Engineering, Computer Science or Biomedical Engineering The research group of Prof. Morgado Dias (Madeira Interactive Technologies Institute - Partner of the Carnegie Mellon University - Portugal program) is looking for PhD students and researchers for: (1) Monitoring and Fault detection in Photovoltaic parks (funded project-1year-possible follow-up with a PhD) (2) 3D Image Processing (funded project-1year- possible follow-up with a PhD) (3) Hardware for Artificial Neural Networks, with emphasis on emerging architectures (PhD, but needs to be externally funded) (4) FPGA prototype for detecting sleep APNEA disorders (PhD – needs to apply to a specific program in Portugal for funding if required) Information about the Institute, University and Region: Madeira Interactive Technologies Institute - Partner of the Carnegie Mellon University - Portugal program, is an innovation institute established as an associated independent non-profit R&D organization whose founding members are the University of Madeira, Madeira Tecnopolo S. A. and Carnegie Mellon University. Recently awarded a 2,4M€ funding, Madeira-ITI operates in the interdisciplinary domain of human-computer interaction encapsulating contributions from computer science, psychology and design in order to address/engage in important scientific and technological challenges that are both relevant to society and have significant economic impact (www.m-iti.org <http://www.m-iti.org> ). The University of Madeira is an active public university integrated in Portuguese Higher Education System and the European Community. Madeira is a small island with paradisiac living conditions and warm people. Recently elected as the Best European Island, you will find in a small place a diversity of landscapes that go from the beach to the mountain with low living costs and almost no crime. Prof. Morgado Dias research expertise includes Artificial Neural Networks, FPGAs and Renewable Energy. More information can be found at: http://www.cee.uma.pt/morgado/ Profile for candidates: (1) A master’s degree in Electrical Engineering, Computer Science, Biomedical Engineering or Computer Engineering (preferred) or an undergraduate degree in the same areas, completed before arrival. (2) Good oral and spoken English as well as good communication skills. TOEFL or IELTS scores are encouraged. At least the English proficiency that fulfills the requirements of CSC applications is necessary. (3) Teamwork spirit. (4) Self-motivation and independent working ability. (5) Knowledge of and research experience related to the above fields are strongly preferred. (6) Previous published work and interest in pursuing a PhD is preferred. Application: If you are interested, please address your motivation letter (with a brief description of yourself, research ability & interest) and CV to: morgadoatuma.pt <mailto:morgado@uma.pt> --- Este email está livre de vírus e malware porque a proteção avast! Antivirus está ativa. http://www.avast.com

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PhD opportunities at the Queensland Brain Institute
by Geoffrey Goodhill Aug. 8, 2014

Aug. 8, 2014

PhD Positions at the Queensland Brain Institute (QBI), The University of Queensland Application deadline Oct 1st 2014 The Queensland Brain Institute (www.qbi.uq.edu.au) is seeking exceptional and highly motivated PhD candidates to undertake neuroscience research at its state-of-the-art building located at The University of Queensland’s (UQ) St Lucia campus, Brisbane, Australia. UQ has 45,000 students (12,000 postgraduate) and is ranked in the top 100 universities in the world. QBI is one of UQ’s flagship research institutes, with researchers working to understand the fundamental mechanisms of brain function, and applying such research to the development of new therapeutics to combat diseases where brain function is compromised. The Institute’s multidisciplinary environment is home to more than 400 staff and students. Key areas of interest include: • Ageing Dementia and Alzheimer’s Disease • Cognition and Behaviour • Computation and Neuronal Circuits • Neurogenesis and Neuronal Survival • Genetics and Epigenetics • Neuronal Development and Connectivity • Sensory Systems • Synaptic Function Since establishment in 2003 QBI researchers have published >1200 papers in the world’s foremost journals, including 460 in the past 2 years. QBI is 100 metres from UQ's Centre for Advanced imaging (www.cai.uq.edu.au) which houses 7T human and 16.4T animal MRI scanners. Scholarships and Remuneration QBI offers full-time PhD scholarships for outstanding Australian and International students (living allowance AU$25,392 per annum, indexed annually, tax exempt) for 3 years with a possible 6 month extension. In addition, QBI supports high quality domestic and international applicants in their nomination to The University of Queensland’s competitive scholarship rounds (three rounds per year). A QBI top-up scholarship of $5,000 per annum may also be available to successful candidates. Candidates should have a First Class Honours degree (or equivalent) in neuroscience, molecular biology, bioscience, psychology, genetics, physical sciences, engineering, mathematics or a related discipline. Strong academic performance, the commitment to conduct high quality neuroscience research, and published output are highly desirable. For further information on the Basis of Admission to a UQ research higher degree, please visit http://www.uq.edu.au/grad-school/our-research-degrees. Successful applicants must accept and commence within 6 months of receiving an award. Enquiries and Applications Please review the research interests of QBI Group Leaders at http://www.qbi.uq.edu.au/group-leaders and make direct contact with a Group Leader working in an area of greatest interest to you, providing your detailed academic resume and complete academic records (including GPA scores/grades, and grading scale details). The Group Leader will work with you to develop your application for submission to QBI. Applications close on 1 October, 2014 Professor Geoffrey J Goodhill Queensland Brain Institute and School of Mathematics & Physics University of Queensland St Lucia, QLD 4072, Australia Email: g.goodhill(a)uq.edu.au http://www.qbi.uq.edu.au/professor-geoffrey-goodhill

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PhD Studentship in Neural Fields and Movement Control, University of Reading, UK
by Ingo Bojak Aug. 7, 2014

Aug. 7, 2014

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Fellowships in Neural Engineering at the University of Washington
by Adrienne Fairhall Aug. 6, 2014

Aug. 6, 2014

We invite applications for the Washington Research Foundation Innovation Fellowships in Neuroengineering at the University of Washington in Seattle. These fellowships will be offered through the recently established UW Institute for Neuroengineering, which aims to aims to solve some of today’s greatest scientific challenges about neural system function by linking advances in biology, computing, devices, data science, and computational neuroscience. These highly selective fellowships will fund research in computational and engineering approaches to neuroscience; joint mentoring between faculty in different disciplines (CS, engineering, mathematics, neurobiology) is strongly encouraged. Applications are due September 1, 2014. Please see http://uwin.washington.edu/post-docs/ for more details of the application procedure.

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IWSP7: Epilepsy Mechanisms, Models, Prediction & Control
by Levin Kuhlmann Aug. 6, 2014

Aug. 6, 2014

Dear Colleagues, It is with unbridled excitement that we announce the date of the 7th International Workshop on Seizure Prediction: IWSP7: Epilepsy Mechanisms, Models, Prediction & Control Date: 3-6 August 2015 Location: Melbourne, Australia Website: www.iwsp7.org<http://www.iwsp7.org> The IWSP series of conferences bring together researchers across multiple disciplines to focus on epilepsy research. This conference will centre on the engineering and science of epilepsy from basic mechanisms and imaging, to computational modelling for analysis and prediction, through to feedback control of epilepsy. Invited Speakers: Samuel Berkovic, University of Melbourne, Australia (Genetics and individual-specific nature of epilepsy) Bin He, University of Minnesota, USA (EEG-based imaging to localise seizure and high frequency activity) Louis Lemieux, University College London, UK (Multi-modal imaging of human epileptic zone and networks) Martha Morrell, Stanford University, Neuropace, USA (Responsive seizure control and clinical implications) John Terry, University of Exeter, UK (Modelling multiscale brain networks: seizure initiation, evolution and prediction) Gregory Worrell, Mayo Clinic, USA (Forecasting epileptic seizures in dogs) Please forward this notice to anyone that you think might be interested. We look forward to seeing you at the meeting! On behalf of the local organizing committee Levin Kuhlmann, PhD David B. Grayden, PhD Mark J. Cook, MD Dean R. Freestone, PhD Alan Lai, PhD Elma O'Sullivan-Greene, PhD Andre Petersen, BSc(hons) Email: organizingcommittee(a)iwsp7.org<mailto:organizingcommittee@iwsp7.org>

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Wanted: neurotheory postdoc, to catch fireflies
by Xaq Pitkow Aug. 6, 2014

Aug. 6, 2014

POSITION: Xaq Pitkow's Computational Neuroscience group jointly at Rice University and the Baylor College of Medicine, in Houston, Texas, is seeking a highly motivated postdoctoral researcher who will develop theory and analyses of dynamic neural representations in animal brains. This is for a newly funded project to test how neural computations enable animals to catch fireflies in a virtual reality environment. The project will require multiple interlocking theoretical components and hands-on engagement with neural data analysis. We aim to understand the interplay between vision, spatial navigation, and decision-making in the face of uncertainty during a naturalistic, continuous-time task. LAB ENVIRONMENT: Our research focuses on computational functions of neural networks, especially how they compute properties of the world using ambiguous sensory evidence. A few related topics include: distributed neural representations, biological algorithms for statistical inference, and computation by nonlinear recurrent networks. This project is a close collaboration with the labs of Dora Angelaki, Greg DeAngelis, and Paul Schrater, all highly quantitative researchers in behavioral and cognitive neuroscience. QUALIFICATIONS: Important selection criteria include strong mathematical skills, practical knowledge of machine learning tools, and past research accomplishments. Applicants must have a Ph.D. in a relevant quantitative discipline, e.g. physics, math, machine learning, or statistics. Previous experience with neurobiology is advantageous but not essential. APPLYING: Applicants should email a letter of research interests and CV to Xaq Pitkow (xaq(a)rice.edu) The position is available immediately, and we look forward to reviewing applications as they arrive.

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Postdoc position in machine/biological vision – Serre lab, Brown University (Providence, RI)
by Thomas Serre Aug. 5, 2014

Aug. 5, 2014

The computational vision research group, headed by Dr. Thomas Serre at Brown University, has an opening for a postdoctoral fellow to work at the interface between computational neuroscience and computer vision. In particular, we are looking for computer scientists interested in the development of novel machine learning / computer vision algorithms derived from high-fidelity representations of cortical microcircuits to achieve human-like performance on complex information processing tasks. Candidates are expected to have a solid background in modern computer vision and machine learning methods and in particular, strong expertise in optimization techniques and deep learning methods. An interest in biological vision and applications of computer vision to the biological sciences would also be a strong plus. The initial appointment is for 12 months, renewable for another year, and potentially longer depending on funding. The start date is negotiable though an early start is strongly preferred. Salary is commensurate with experience and is competitive. Representative recent work related to the project: D. Reichert & T. Serre. Neuronal synchrony in complex-valued deep networks. International Conference on Learning Representations, 2014 T. Serre and T. Poggio. Reverse-engineering the brain. In: the Communications of the Association for Computing Machinery (CACM), 53(10), pp. 54-61, Oct 2010 Research group: Our research group is located within the Department of Cognitive Linguistic & Psychological sciences at Brown University. We maintain strong ties with the neuroscience, computer science, engineering and applied math departments as part of the Brain Institute. Through Brown’s Center for Computation and Visualization (https://www.ccv.brown.edu) our group has access to a state-of-the-art computing facility with includes over 300K GPU cores and over 500 Teraflops of GPU computing power. Information about Dr. Serre and his research group can be found at http://serre-lab.clps.brown.edu . Requirements: Applicants are expected to have finished, or be about to finish their Ph.D. degrees. They must have a strong background in computer vision and/or machine learning, with a track record of relevant publications at top venues (such as NIPS, CVPR, ICCV, ICML or ECCV). Excellent programming skills are required (C/C++/Matlab/Python). Programming experience in CUDA and/or parallel computing would be a strong plus. Application: Please send your applications by email to serre-admin(a)correct_university_name.edu where correct_university_name should be replaced by brown. Please include a brief statement of interests, a curriculum vita, a list of publications and contact details for 2-3 letters of reference (no letters required at this stage).

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