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Faculty Position in Computational Neuroscience at the University of California, Irvine
by Jeff Krichmar Sept. 14, 2014

Sept. 14, 2014

Dear Computational Neuroscientists, The following job position may be of interest to many of you. ************************** University of California, Irvine Faculty Position in Computational Neuroscience The Department of Cognitive Sciences (www.cogsci.uci.edu) at the University of California, Irvine (UCI) invites applications for a faculty position at the Associate or Full Professor level. We are especially interested in candidates who use mathematical, computational, or robotics approaches to study the neural basis of cognition in any of these areas: (1) vision, hearing, and attention; (2) memory and decision-making; (3) learning and development; (4) language. Applicants whose research relates to human behavior are preferred. A strong record of publications and extramural funding is essential. Exceptional candidates at the Assistant Professor level will also be considered. The online application includes: A cover letter, CV, research and teaching statements, 3 recent publications, and contact information for 3-5 referees. Interested candidates can apply for the position at: https://recruit.ap.uci.edu/apply/JPF02452. To ensure full consideration, please complete the application by November 15, 2014. The University of California, Irvine is an Equal Opportunity/Affirmative Action Employer committed to excellence through diversity. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, age, protected veteran status, or other protected categories covered by the UC nondiscrimination policy. ************************** Best regards, Jeff Krichmar Department of Cognitive Sciences 2328 Social & Behavioral Sciences Gateway University of California, Irvine Irvine, CA 92697-5100 jkrichma(a)uci.edu http://www.socsci.uci.edu/~jkrichma

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postdoctoral position in Systems Neuroscience at the University of Pennsylvania
by Maria Geffen Sept. 12, 2014

Sept. 12, 2014

POSTDOCTORAL POSITION IN SYSTEMS NEUROSCIENCE University of Pennsylvania, Philadelphia PA A postdoctoral position is available in the Laboratory of Auditory Coding under direction of Dr. Maria Neimark Geffen at the University of Pennsylvania School of Medicine to study the neural mechanisms of hearing and emotional learning in the auditory cortex of awake rodents. We invite applications from talented, energetic and enthusiastic candidates with (or expecting) a Ph.D. in life sciences. The techniques include electrophysiological recordings, imaging and optogenetics in awake behaving rodents. Experience in either electrophysiology, imaging or behavioral studies is required. Computational skills are a plus. Please visit http://www.med.upenn.edu/hearing for more information. Our laboratory is part of the Hearing Sciences Center a at the Department of Otorhinolaryngology, Department of Neuroscience and the Mahoney Institute of Neurological Sciences. University of Pennsylvania has a very interactive and diverse systems neuroscience community. The University of Pennsylvania is within walking distance of the downtown of Philadelphia, which provides for a vibrant cultural urban environment. Please submit your application, including a CV, a brief statement of past and future research interests, links to recent publications, and recommendation letters (a letter from Ph.D. advisor is required) to Dr. Maria Neimark Geffen at mgeffen(a)med.upenn.edu .

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Henry Rutgers Term Chair in Computer and Data Science
by Bart Krekelberg Sept. 11, 2014

Sept. 11, 2014

Rutgers University - Newark (RU-N) seeks outstanding candidates to serve as Henry Rutgers Term Chair in Computer and Data Science, with a specific focus on computational methods in machine learning, or statistical modeling of complex data sets in the sciences, engineering, business, or medical fields. Recruitment is at the associate professor level; however, exceptional candidates qualifying for the full professor rank may be considered. This recruitment is part of the Rutgers University Strategic Plan to strengthen Rutgers leadership in burgeoning fields, and part of the new transdisciplinary research Institute for Data Science, Learning, and Applications (I-DSLA). I-DSLA brings together computer scientists, cognitive scientists, philosophers, and domain experts in neuroscience, environmental science, chemistry, medicine, business, and other professions. The new hire will be poised to leverage these experts as well as a new high performance computing cluster on the RU-N campus, to advance knowledge about learning from data. Applicants should hold a Ph.D. in Computer Science, Computational Statistics, or a related field and have a record of excellent scholarship, as demonstrated by significant publications and funding. They should be open to collaborative research within the RU-N community and will be expected to help build a group of faculty in areas related to I-DSLA, maintain an active externally-funded research program, teach undergraduate and graduate courses, and mentor Ph.D. students. Rutgers, The State University of New Jersey, is a leading national public research university and the state's preeminent, comprehensive public institution of higher education. Rutgers, the State University of New Jersey, is an Equal Opportunity / Affirmative Action Employer. Qualified applicants will be considered for employment without regard to race, creed, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability status, genetic information, protected veteran status, military service or any other category protected by law. As an institution, we value diversity of background and opinion, and prohibit discrimination or harassment on the basis of any legally protected class in the areas of hiring, recruitment, promotion, transfer, demotion, training, compensation, pay, fringe benefits, layoff, termination or any other terms and conditions of employment. Applicants should apply no later than November 1st, 2014. To receive detailed instructions on how to apply, please email hrsearch(a)rutgers.edu with the subject line 'How to apply'. --- Bart Krekelberg, PhD Associate Director, Center for Molecular and Behavioral and Neuroscience Associate Director, Rutgers Brain Imaging Center Associate Professor, Rutgers University Newark, NJ 07102 USA T: 973 353 3602 W: vision.rutgers.edu E: bart(a)vision.rutgers.edu

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Postdoctoral position available in the Niv Lab at Princeton
by Yael Niv Sept. 10, 2014

Sept. 10, 2014

A postdoctoral position is available in the lab of Dr. Yael Niv in the Princeton Neuroscience Institute and the Department of Psychology at Princeton University (http://www.princeton.edu/~nivlab) for a collaborative project with the lab of Geoffrey Schoenbaum at NIDA. Research in the Niv lab focuses on computational modeling of learning and decision making at the systems level (reinforcement learning, Bayesian inference), and model-driven behavioral and functional imaging experiments of human learning and decision making. Research in the Schoenbaum lab utilizes rodent models to study learning and decision making, as well as addiction. We seek an exceptionally talented candidate who is intensely interested in understanding decision making and addiction, has proven experience in empirical investigations of learning and decision making (and corresponding task design), has expertise in computer programming and modeling, and has a keen interest in reinforcement learning and normative models of behavior. Background in studies of addiction, functional imaging and/or rodent work is an advantage. Anticipated start date is January 2015 though there is considerable flexibility. This is a one-year position with the possibility of renewal, pending satisfactory performance. Essential Qualifications: PhD in psychology, neuroscience or equivalent. Proven experience with computer programming. Preferred Qualifications: The ideal candidate will have robust experience with fMRI (event related designs and model-based analysis techniques) or with studies of rodent decision making (Pavlovian and instrumental conditioning), will be proficient in programming (Matlab or equivalent), and will have experience with computational modeling (machine learning, reinforcement learning, Bayesian models). To apply, please visit the website https://jobs.princeton.edu (requisition # 1400567) and create an online application. Applicants must submit a cover letter stating background and research interests and citations of at least two representative publications, a CV, and contact information for at least two references. Princeton University is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law. This position is subject to the University’s background check policy.

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Joint Neuroscience-Math tenure-track faculty position at UT Austin
by Ila Fiete Sept. 9, 2014

Sept. 9, 2014

Dear Colleagues, UT Austin is initiating a multi-faculty (cluster) search for tenure-track faculty in computational and theoretical neuroscience, to build on existing efforts here in that direction. I request that you pass on word of this search to exceptional senior postdocs who bridge math and neuroscience and are ready to start junior faculty positions. Attached below is the text of the search advertisement, and details on how to apply. Best regards, Ila --------------- The Departments of Neuroscience and Mathematics at the University of Texas at Austin are seeking a leader in the field of mathematical neuroscience or mathematics applied to neuroscience, for appointment as a tenure-track assistant professor. Areas of interest include, but are not limited to, the intersection of neuroscience with dynamical systems, partial differential equations, algebraic geometry, statistics, information theory, stochastic processes, and applied and computational topology. This search is part of a multi-faculty hiring initiative in Computational and Theoretical Neuroscience at UT Austin -- including a position in Psychology and a joint position between Neuroscience and Computer Science, with the aim of building on existing strengths in the field to create a Computational and Theoretical Neuroscience center. The successful candidate will orchestrate an outstanding program of independent research and contribute to an environment of diversity in higher education through research, teaching, and service. She or he will have joint membership in both departments and teach one course in each department. The position will include full access to the Texas Advanced Computing Center supercomputers, and the option to join various multi-disciplinary advanced computing and neuroscience centers on campus. Qualified applicants with a Ph.D. or equivalent and two or more years of post-graduate experience will be considered for appointment. Applications will be reviewed starting September 5, 2014, until the position is filled. Please submit applications through the MathJobs website: https://www.mathjobs.org/jobs/jobs/6059 <https://www.mathjobs.org/jobs>. A complete application will include: 1. Statement of research interests. 2. Curriculum vita. 3. Letters of reference from at least 3 individuals. 4. Statement of teaching philosophy and experience, with evidence of teaching excellence. UT Austin is an equal opportunity employer committed to excellence through diversity. We strongly encourage applications from all qualified individuals including women and minorities. UT Austin is responsive to the needs of dual career couples and is dedicated to work-life balance through an array of family-friendly policies (http://www.utexas.edu/provost/policies/family/) For further inquires related to this search, please email Kathleen Pantalion (kpantalion(a)mail.clm.utexas.edu <mailto:kpantalion@mail.clm.utexas.edu>). -- ________________________________ Ila Fiete Associate Professor Center for Learning and Memory The University of Texas at Austin Phone: 512.232.8439

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Job Posting: Neuroscience Image Processing Scientist at Northwestern University
by Xue Wang Sept. 9, 2014

Sept. 9, 2014

The Department of Radiology at Northwestern University Feinberg School of Medicine is seeking a full-time Neuroscience Image Processing Scientist. Under the direction of the Medical Director of the Neurological Testing Center and the Director of Neuroimaging Research, the Neuroscience Image Processing Scientist will be responsible for coordinating imaging for use in epilepsy surgery and other functional neurosurgical procedures. Assignments will require close interaction with neuroradiology and neuroimaging collaborators to develop and implement more advanced functional imaging techniques. The primary effort will be focused on the development and implementation of clinical tools; however, there is dedicated time for independent research. Northwestern has very active clinical and research programs in Neurology and Neuroimaging. Imaging resources include 10 clinical and 4 dedicated research high field MRI systems, a PET/CT system, high density EEG and TMS. There is a very active and rapidly expanding Neurosurgery group with strong collaborations with BrainLab, and a supportive clinical environment for research. Candidates should have an MD or PhD in neuroscience, medicine, physics, psychology or a related field, and excellent communication skills. Experience with analysis of structural, diffusion, fMRI including resting state, MEG, PET, SPECT, and EEG data is required. The ability to co-register multi-modality diagnostic (EEG, MEG) studies with SISCOM, MRI, fMRI, DTI and PET studies is a must. Experience in planning stereo EEG electrode placement, surgical guidance, and proficiency with PRISM software, CURRY Neuroimaging Suite, and BrainLab surgical navigation systems, is desired. The position is funded for an initial two-year time period, with a high likelihood of long term employment. Salary is based on the NIH scale and benefits are highly competitive. Position is available on July 1, 2014, and applications will be accepted until the position is filled. Applicants should send curriculum vitae, references and cover letter to: Todd B. Parrish, Ph.D. Department of Radiology 737 N. Michigan Ave, 16th Floor Chicago, IL 60611 toddp(a)northwestern.edu Northwestern University is an Equal Opportunity, Affirmative Action Employer of all protected classes, including veterans and individuals with disabilities. Women and minorities are encouraged to apply. Hiring is contingent upon eligibility to work in the United States

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2014 SFN Meeting Workshop: NSG Portal for parallel simulations
by Ted Carnevale Sept. 9, 2014

Sept. 9, 2014

What: Using the Neuroscience Gateway Portal for Parallel Simulations A Satellite Symposium at the 2014 Society for Neuroscience Meeting Where: Location to be announced in downtown Washington, DC When: 9 AM - Noon on Saturday, November 15, 2014 Speakers to include: A. Majumdar, S. Sivagnanam, and T. Carnevale Registration deadline: Friday, October 31, 2014 Do you have a large scale modeling project that exceeds the speed or capacity of your local hardware? Have you tried to use high performance computing (HPC) resources at your own institution, but found the process too difficult because of administrative or technical barriers? If yes, this workshop is for you. In a single morning session, you will learn how to use the Neuroscience Gateway Portal (NSG) http://www.nsgportal.org/ which is designed for neuroscientists who need to use HPC resources for large modeling projects. It simplifies every aspect of the process, from getting allocations of free CPU time to uploading your model, launching and monitoring jobs, and downloading results. The NSG already has several parallel simulators installed, including Brian, NEST, NEURON, PGENESIS and PyNN. Space is limited, so sign up quickly. See http://www.neuron.yale.edu/neuron/static/courses/nsg2014/nsg2014.html for further details and the registration form. Support: The NSG project is supported by a grant from the National Science Foundation. NSG provides its users free allocations of CPU time on HPC resources under the auspices of XSEDE (Extreme Science and Engineering Discovery Environment), which is also supported by NSF.

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PhD position in Neurocomputational modeling of language processing
by Hartmut Fitz Sept. 8, 2014

Sept. 8, 2014

A PhD position is available at the Max-Planck Institute for Psycholinguistics in Nijmegen, the Netherlands, within the 'Language in Interaction' consortium. The PhD candidate will participate in a team effort to understand the neurobiology underlying language processing by building computer models for sentence processing based on recurrent networks of spiking neurons. The goal is to develop a model with processing memory based on vector representations of words which are incrementally interpreted in terms of thematic roles ("who did what to whom"). A core objective will be to investigate the computational role of different neuronal models, connectivity types, neurobiologically motivated adaptation mechanisms, and different methods for calibrating the model's read-out units. Closing date: 30 September 2014 For more information see: https://www.languageininteraction.nl/jobs/phd-positions.html https://www.languageininteraction.nl/jobs/id-2nd-phd-call-general.html

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Postdoc opportunity at Stanford in Causal Brain Network Analysis
by Wei Wu Sept. 5, 2014

Sept. 5, 2014

Dear fellow researchers, The Etkin Lab at Stanford University is currently seeking to fill a postdoc position for causal brain network analysis. Please find below the job requirement and contact info. Thank you for your attention! Postdoc opportunity at Stanford in Causal Network Analysis – from Concurrent TMS/fMRI to Transdiagnostic Imaging of Psychiatric Disorders The Laboratory of Amit Etkin, MD PhD at Stanford University is currently accepting applications for a postdoctoral research fellowship focused on causal network analyses using fMRI in healthy individuals and patients with a range of psychiatric disorders. The focus of this position is on application of network analysis tools (e.g. graph theory, network-based statistics, etc) to fMRI data, including both causal circuit mapping experiments with concurrent transcranial magnetic stimulation (TMS) and fMRI, as well as resting-state data from patients across the mood, anxiety and stress disorders represented, and across the lifespan. The successful applicant will have a PhD in Cognitive Neuroscience, Neurophysiology, Psychology, Computer Science, Statistics or related fields. Experience with analysis of fMRI data or with causal network analysis tools is required. Additional programming experience is a plus. Duties will also include manuscript preparation, presentation of findings at conferences, management of research assistants and contribution to the preparation of grants. Laboratory and Stanford resources include research-dedicated 3T and 7T MRI scanners, concurrent TMS/fMRI setups and concurrent TMS/EEG setups. Salary commensurate with experience. More information about our ongoing studies can be found at: http://etkinlab.stanford.edu. To apply, please send a curriculum vitae, a statement describing research interests and relevant background and three letters of recommendations, as well as relevant reprints/preprints of research articles to: Amit Etkin, MD, PhD Department of Psychiatry and Behavioral Sciences Stanford University amitetkin(a)stanford.edu

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New software for neuronal model fitting
by Szabolcs Káli Sept. 5, 2014

Sept. 5, 2014

Dear Colleagues, We are pleased to announce the availability of the first public (beta) version of Optimizer, an interactive software tool for fitting the parameters of neuronal models. Optimizer was developed in Python, and has the following main features: - efficiently fits the free parameters of arbitrary neural (or other) models using advanced nonlinear optimization algorithms - all aspects of the optimization problem (including the model, its free parameters, the target data, the simulation and recording protocol, the error function) and the fitting process itself can be conveniently controlled from a graphical user interface (GUI) - full internal support for models implemented in the Neuron simulator (while other models can be also optimized using a "black box" approach) - choice of 7 different (tunable) optimization algorithms, including a customized evolutionary (genetic) algorithm, differential evolution, simulated annealing, the basinhopping algorithm, random search, and two local search methods - choice of 11 different error functions for the comparison of simulated and experimental (target) data, including generic ones as well as several feature-based ones which are appropriate for the comparison of membrane potential traces containing action potentials; error functions can be combined with arbitrary weights - the software is easily extensible in various ways to support novel scenarios A paper describing the software, several use cases, and comparisons with other model fitting tools, has been recently published in Frontiers in Neuroinformatics (the abstract and citation details are included at the end of this message): http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00063/abstract The software can be downloaded using git from https://github.com/vellamike/optimizer where you can also find instructions for installing the software and its dependencies. The current version of Optimizer has been verified to work under several recent Linux distributions. Alternatively, a compressed archive containing the software and various examples can be downloaded from https://github.com/vellamike/optimizer/releases/tag/v2.0-beta The documentation of the software is available at http://optimizer.readthedocs.org/en/latest/ Finally, we wish to encourage everyone interested in fitting neuronal models to try the software and provide feedback. If you find that your favorite optimization algorithm or error function is not currently included in Optimizer, it can probably be added with relatively little effort, especially given an existing implementation of the algorithm or function itself, and we would be very interested in collaborating on such an extension. We are also interested in learning about the model fitting problems encountered by potential users; we would be happy to advise users on whether and how Optimizer may be used to solve particular problems; in addition, future development of the software will be guided by the feedback that we receive. Best regards, Szabolcs Káli and Péter Friedrich Friedrich P, Vella M, Gulyás AI, Freund TF and Káli S (2014) A flexible, interactive software tool for fitting the parameters of neuronal models. Front. Neuroinform . 8 :63. doi: 10.3389/fninf.2014.00063 Abstract: The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation) of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problems of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire) neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool.

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