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Cognitive, Computational, and Systems Neuroscience Training Program at University of Oregon
by Luca Mazzucato Nov. 27, 2018

Nov. 27, 2018

The Institute of Neuroscience (ION) at the University of Oregon is welcoming applications for graduate training in neuroscience (deadline December 1st 2018). The training program in Cognitive, Computational, and Systems Neuroscience ( CCSN <http://ion.uoregon.edu/content/cognitive-computational-and-systems-neurosci…>) is sponsored by the University of Oregon Institute of Neuroscience, the nation's first research institute of its kind. In keeping with the innovative origins of the Institute, the CCSN program provides a rare opportunity for graduate students to forge experimental and theoretical links between systems and cognitive neuroscience while becoming experts in their field of choice. We believe that such interdisciplinary training positions our graduates to become leaders in the growing field of integrative and behavioral neuroscience. The Computational Neuroscience core of the CCSN includes Yashar Ahmadian's and Luca Mazzucato's labs, in close collaboration with all other ION labs (Chris Doe, Santiago Jaramillo, Bryce Kuhl, Shawn Lockery, David McCormick, Cris Niell, Matt Smear, Mike Wehr, and many others). The heart of our program is thesis research. Twenty eight faculty members drawn from the departments of Biology, Psychology, and Mathematics serve as research mentors and instructors. These researchers use a wide range of approaches including molecular biology, genetics, electrophysiology, optogenetics, functional brain imaging, and computational neuroscience to tackle fundamental questions in the neuronal basis of perception, behavior, memory, and cognition. CCSN faculty study not only humans but a wide range of model organisms including C. elegans, Drosophila, zebrafish, and rodents. As a result, students enrolled in our PhD program come away with the broad conceptual and technical skills necessary to run an independent neuroscience research lab or pursue many other related career paths. Our state-of-the-art facilities and excellent support staff allow ION members to progress rapidly by making exploratory or pilot experiments accessible. When not "at the bench," trainees follow a customizable course of study that leads from a year-long graduate core course to a variety of elective courses that allow students to go deeper into their specialty. In addition to course work, trainees learn in a variety of interactive settings including lab meetings, journal clubs, seminars, bi-monthly research-in-progress meetings, and institute retreats. These activities, together with a culture of openness and engagement among CCSN faculty members, create a degree of cohesiveness that sets CCSN apart from programs at other universities. We're passionate about creating an inclusive environment that promotes and values diversity. Research institutions that are diverse in gender, gender identity, race, sexual orientation, ethnicity, age, and perspective are proven to foster greater creativity and promote synergistic, collaborative innovation and interactions. Our application deadline is December 1, 2018. If interested in applying, please contact David McCormick (davidmc(a)uoregon.edu) David A. McCormick Director, Institute of Neuroscience davidmc(a)uoregon.edu

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2 openings in vision rehabilitation and multi-modal data modelling: 1 PhD and 1 RA/postdoc
by Paul Matusz Nov. 27, 2018

Nov. 27, 2018

Institute of Information Systems at HES-SO in Valais and Foundation for the Blind in Lausanne (Switzerland) are looking for 1 PhD student and 1 RA/postdoc (open rank) interested in links between (eye) health, neuroscience and modelling links between sensory/ perceptual - brain - cognitive processes. Our project aims to asssess the potential of virtual reality games in the rehabilitation of the most common pediatric vision disorder - amblyopia ("lazy eye"). The recovery potential is assessed at the level of both the usual, basic vision skills (visual acuity or 3D vision) and functional "visual" skills like age-appropriate literacy skills. The project likewise aims to establish the role therein of different selective attention systems (visual, multisensory), as studied with rigorous experimental paradigms combined with the advantages of EEG and multi-variate EEG/ERP analyses (eg. Murray et al. 2008 BTOP; Tivadar & Murray 2018 ORM tinyurl.com/y8uxonnf ). The project is well-suited for students interested in modelling multi-modal clinical pediatric data and clinical change in the areas of medicine, neuroscience and/or cognition. More details here: https://drive.google.com/file/d/1VpzIFBz1wzrqHQNqEsD3CnktbD2axGgv/view Paul (Pawel) Matusz, Ph.D. Junior Group Leader Institute of Information Systems, HES-SO – Radiology, University Hospital Centre & University of Lausanne, Sierre – Lausanne, Switzerland Adjunct Assistant Professor Hearing & Speech Sciences, Vanderbilt University, Nashville, TN, USA https://groupforrealworldneuroscience.wordpress.com/ https://twitter.com/paweljmatusz +41 213141547

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Graduate Program in Computational Neuroscience at Tübingen - Deadline Jan 15th
by Philipp Berens Nov. 27, 2018

Nov. 27, 2018

The Master program "Neural Information Processing - Computational Neuroscience" covers theoretical and computational aspects of neuroscience. Faculty include: Peter Dayan, Matthias Bethge, Zhaoping Li, Martin Giese, Alexander Ecker, Philipp Berens, Fabian Sinz, Anna Levina and many more! Students obtain extensive training in computational modeling of neural systems, machine learning data analysis and neuroscience. While the first year is dedicated to course work at the graduate level, the second year provides hands-on research experience in leading labs in lab rotations and during thesis work. After finishing the Master program, students can smoothly transition to our PhD program and continue their research. The program provides research-oriented training in a wide spectrum of basic computational neuroscience topics with different options: machine learning for neuroscience and neural data analysis models of neural dynamics and coding motor control, rehabilitation robotics and brain-computer interfaces systems neuroscience and neurophysiology data science, bioinformatics and programming behaviour and cognition The deadline for applications is January, 15th (written documents must be in Tübingen). For more information please visit: https://www.bccn-tuebingen.de/education/master-of-science-neural-informatio… Please forward to interested students at the BSc level. -- Prof. Dr. Philipp Berens University of Tübingen Institute for Ophthalmic Research http://www.berenslab.org phone: +49-7071-2988833

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Three Postdocs in Epilepsy and Brain-Computer Interfaces, Melbourne, Australia
by David Grayden Nov. 27, 2018

Nov. 27, 2018

Three Postdocs in Epilepsy and Brain-Computer Interfaces ARC Training Centre in Cognitive Computing for Medical Technologies The University of Melbourne and IBM Research - Australia http://jobs.unimelb.edu.au/caw/en/job/898108/research-fellows-in-epilepsy-3… We have created a new research training centre dedicated to Cognitive Computing for Medical Technologies and are now looking for outstanding Research Fellows to join on us on three-year appointments. This is an exciting joint venture between the Department of Biomedical Engineering and the School of Computing and Information Systems in collaboration with our research partners IBM Research Australia, University of South Australia, Western Sydney University, RMIT University, and CSIRO. Our focus is to create a workforce that is expert in developing, applying, and integrating artificial intelligence technologies in data-intensive medical contexts by facilitating the next generation of data-driven and machine learning-based medical technologies. The Three Roles: 1. Epileptic Seizure Prediction - in this project, you will work to develop and apply state-of-the-art machine learning approaches for epileptic seizure prediction as well as assessing the efficacy of epilepsy medication. 2. Epilepsy Modelling - in this project, you will help understand the causes and behaviours of epileptic seizures and develop high fidelity models for seizure prediction. This will be done by bringing together neural models of the brain with machine learning. 3. Brain-Computer Interfaces - in this project, you will work to develop a neural network-based brain-computer interface that can be used to control augmentative devices for people with speech, vision or motor disorders. We are interested in applications from those whose expertise aligns with and reflects strengths in Biomedical Engineering, Electrical Engineering, Mathematics and Computer Science. We are also interested in specific experience in programming skills, machine learning, signal processing and computational neuroscience. Applications close: 07 Jan 2019 11:55 PM AUS Eastern Daylight Time Details: http://jobs.unimelb.edu.au/caw/en/job/898108/research-fellows-in-epilepsy-3… Regards, David Professor David B. Grayden Head of Department Dept of Biomedical Engineering Melbourne School of Engineering The University of Melbourne, Victoria 3010 Australia http://www.bme.unimelb.edu.au | https://www.linkedin.com/in/dgrayden

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open rank faculty position in Human Movement Science at Northeastern, Boston, MA
by Marta Russo Nov. 26, 2018

Nov. 26, 2018

The Department of Biology at College of Science and the Department of Physical Therapy, Movement and Rehabilitation Science at the Bouve College of Health Sciences invites nominations and applications for a tenure track faculty position (open rank, from the Assistant to the Full Professor rank). Applicants are encouraged to submit their application by November 29, 2018 to receive full considerations, but review of applications will continue until the position is filled. More info can be found below or here <https://neu.peopleadmin.com/postings/57607.>. Informal inquiries can be addressed to: Dr. Dagmar Sternad, Chair of the Search Committee (d.sternad(a)northeastern.edu) Apologies for multiple posting. -- Marta Russo, PhD Postdoctoral Research Associate Action Lab Office: 503 Richards Hall Mail: 134 Mugar Life Sciences Building Northeastern University 360 Huntington Avenue Boston, MA 02115 m.russo(a)northeastern.edu *Faculty Position in Human Movement Science* *Northeastern University, Boston, MA* *Assistant, Associate, or Full Professor* *A tenure-track position is open in Human Movement Sciences in the Department of Biology, College of Science and the Department of Physical Therapy, Movement and Rehabilitation Science, Bouve College of Health Sciences, at Northeastern University.* The position is open for applicants from the Assistant to the Full Professor rank. The candidate’s research can be in the areas of experimental, computational, or clinical human movement, with a possible bridge to prosthetics and robotics. This position is part of a growing university-wide cluster in the science of human movement, spanning research strengths from basic experimental and computational research to rehabilitation science, robotics, and neuroengineering. Priority will be placed on research strengths that complement existing strengths in computational human movement science, rehabilitation science, neurophysiology, neuroimaging, and control theory, although excellence is of top-most priority. Depending on the research profile, an affiliated appointment with Bioengineering or Electrical and Mechanical Engineering is possible. The anticipated start date is Fall Semester, 2019. Responsibilities will include conducting an independent, externally funded research program, teaching undergraduate and graduate courses, and participating in departmental, college and university service. Qualified candidates should have experience in, or be able to demonstrate a commitment to, working with diverse student populations and/or in a culturally diverse work and educational environment. The Department of Biology is strongly interdisciplinary, with 20 tenured and tenure-track faculty in Biology and an additional 9 faculty with joint appointments in other departments. The department administers programs in the College of Science at Northeastern University for 1,200 undergraduates, and 100 students in Ph.D., Masters, and Professional Masters programs. The Department of Physical Therapy, Movement and Rehabilitation Sciences, located within Bouvé College of Health Sciences, is comprised of 10 tenured and tenure track faculty and 20 clinical faculty. The Department offers a Ph.D. program in Human Movement and Rehabilitation Sciences, as well as one of the oldest and largest Doctorate in Physical Therapy (DPT) programs in the country. Grounded in its signature co-op program, Northeastern provides unprecedented global experiential learning opportunities. Northeastern University occupies a vibrant 67-acre campus in the heart of Boston, surrounded by other leading educational, health care, technological, and research institutions, as well as world-renowned venues for art and music. Applicants must have a PhD in the life sciences, including physical medicine and physical therapy, neuroscience, neurophysiology, bioengineering, electrical or mechanical engineering, kinesiology, rehabilitation sciences, or other related disciplines. All applicants should have a strong record of scholarly accomplishment that demonstrate research productivity and the ability to perform cutting edge research. Candidates seeking appointment at the associate or full professor level should have substantial research productivity and a history of grant support and academic service. *Please submit a curriculum vitae, cover letter, a two-page description of research accomplishments and plans, a statement on teaching, and the name and contact information for three references through the application portal* https://neu.peopleadmin.com/postings/57607 <https://na01.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense…> . Review of applications will begin immediately and will continue until the position is filled. Applications should be submitted by November 29, 2018 to receive full considerations. Informal inquiries can be addressed to: Dr. Dagmar Sternad, Chair of the Search Committee (d.sternad(a)northeastern.edu) -- <http://www.hsantalucia.it/5x1000-sostieni-la-nostra-ricerca> *Rispetta l'ambiente: se non ti è necessario, non stampare questo messaggio - **Please consider the environment before printing this email.*

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Using spike train distances to identify the most discriminative neuronal subpopulation
by Thomas Kreuz Nov. 26, 2018

Nov. 26, 2018

Dear all, may I kindly draw your attention to our most recent paper which contains several new algorithms to address neuronal population coding using spike train distances: Satuvuori E, Mulansky M, Daffertshofer A, Kreuz T: Using spike train distances to identify the most discriminative neuronal subpopulation <https://www.sciencedirect.com/science/article/pii/S0165027018302747> JNeurosci Methods, 308, 354 [PDF <https://www.sciencedirect.com/science/article/pii/S0165027018302747>] and arXiv [PDF <https://arxiv.org/pdf/1805.10892.pdf>] (2018). For the abstract see below. This paper is part of the dissertation "Spike train distances and neuronal coding <http://dare.ubvu.vu.nl/bitstream/handle/1871/55855/abstract%20english.pdf?s…>" of my PhD student Eero Satuvuori whose full thesis can now be found here <http://dare.ubvu.vu.nl/bitstream/handle/1871/55855/abstract%20english.pdf?s…>. Besides some original parts and the paper cited above it also contains these recent works: Satuvuori E, Kreuz T: Which spike train distance is most suitable for distinguishing rate and temporal coding? JNeurosci Methods 299, 22 [PDF <https://ac.els-cdn.com/S0165027018300372/1-s2.0-S0165027018300372-main.pdf?…>] and arXiv [PDF <https://arxiv.org/pdf/1708.07508.pdf>] (2018). Kreuz T, Satuvuori E, Pofahl M, Mulansky M: Leaders and followers: Quantifying consistency in spatio-temporal propagation patterns New J. Phys., 19, 043028 [PDF <https://doi.org/10.1088/1367-2630/aa68c3>] and arXiv [PDF <https://arxiv.org/pdf/1610.07986v4.pdf>] (2017). Satuvuori E, Mulansky M, Bozanic N, Malvestio I, Zeldenrust F, Lenk K, Kreuz T: Measures of spike train synchrony for data with multiple time-scales JNeurosci Methods 287, 25 [PDF <https://doi.org/10.1016/j.jneumeth.2017.05.028>] and arXiv [PDF <https://arxiv.org/pdf/1702.05394.pdf>] (2017). All the best, Thomas Kreuz PS: Satuvuori E, Mulansky M, Daffertshofer A, Kreuz T: Using spike train distances to identify the most discriminative neuronal subpopulation <https://www.sciencedirect.com/science/article/pii/S0165027018302747> JNeurosci Methods, 308, 354 [PDF <https://www.sciencedirect.com/science/article/pii/S0165027018302747>] and arXiv [PDF <https://arxiv.org/pdf/1805.10892.pdf>] (2018). Abstract: Background Spike trains of multiple neurons can be analyzed following the summed population (SP) or the labeled line (LL) hypothesis. Responses to external stimuli are generated by a neuronal population as a whole or the individual neurons have encoding capacities of their own. The SPIKE-distance estimated either for a single, pooled spike train over a population or for each neuron separately can serve to quantify these responses. New method For the SP case we compare three algorithms that search for the most discriminative subpopulation over all stimulus pairs. For the LL case we introduce a new algorithm that combines neurons that individually separate different pairs of stimuli best. Results The best approach for SP is a brute force search over all possible subpopulations. However, it is only feasible for small populations. For more realistic settings, simulated annealing clearly outperforms gradient algorithms with only a limited increase in computational load. Our novel LL approach can handle very involved coding scenarios despite its computational ease. Comparison with existing methods Spike train distances have been extended to the analysis of neural populations interpolating between SP and LL coding. This includes parametrizing the importance of distinguishing spikes being fired in different neurons. Yet, these approaches only consider the population as a whole. The explicit focus on subpopulations render our algorithms complimentary. Conclusions The spectrum of encoding possibilities in neural populations is broad. The SP and LL cases are two extremes for which our algorithms provide correct identification results. -- Institute for complex systems, CNR Via Madonna del Piano 10 50119 Sesto Fiorentino (Italy) Tel: +39-349-0748506 Email: thomas.kreuz(a)cnr.it Webpage: http://www.fi.isc.cnr.it/users/thomas.kreuz/

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Frontiers Research Topic "Discrimination of Genuine and Posed Facial Expressions of Emotion"
by Jian Liu Nov. 26, 2018

Nov. 26, 2018

Call for Papers: Frontiers Research Topic "Discrimination of Genuine and Posed Facial Expressions of Emotion" More information: https://www.frontiersin.org/research-topics/9221/discrimination-of-genuine-… About this Research Topic Facial expressions demonstrate one emotional states in interpersonal situations. Evidence shows that part of the facial display reflects the emotional experience that is literally felt by the expresser. Interestingly, human beings are capable of identifying facial expressions of the felt emotions as a form of intentional deception to conduct social interaction and to stage displays that have the support of others. Staged or posed facial expressions implement an emotion that an expresser intends to convey, where genuine expressions are considered as the companion of spontaneous emotional expressions. The ability to differentiate genuine displays of emotional experience from posed ones is very important for dealing with day-to-day social interactions. Recent work has been conducted on whether or not people can distinguish between posed and genuine displays of emotion. In spite of few studies to investigate this ability, most prior research suggests that people have the ability to judge genuine and posed facial displays. Unfortunately, previous research has suffered from two major shortcomings: (1) the mixture of staged and genuine displays due to the lack of accounting for possible effects of intentional manipulation, and (2) struggling to consider dynamic aspects when people prepare facial stimuli for experimental investigation. This Research Topic encourages the submission of theoretical and experimental perspectives to broaden understanding of the importance of the discrimination of genuine and posed facial expressions of emotion. These may be new theoretical approaches, those from other disciplines of psychology not usually utilized within the discrimination of genuine and staged emotion identification or new theories and designs. We seek articles that present new hypotheses, concepts, experimental observations, and theories or models; demonstrate how theories adapted from other disciplines may be utilized for emotion identification or provide recommendations to improve current models or theories to enhance their capacity. We look for papers that may combine critical analysis of current models, synthesis of early empirical work by single- or multi-modality analysis (e.g. video, ECG, EEG, fMRI, and single cells in humans and/or animals), and explore the potential for their development applied to the context of emotion recognition. Submissions of systematic reviews and meta-analysis should discuss and promote comprehensive approaches to update and evolve concepts, hypotheses, and theories with potential applications in the community. Keywords: face expression, discrimination, genuine, posed, emotion Guest editors: Huiyu Zhou, Department of Informatics, University of Leicester, United Kingdom. Caroline Ling Li, School of Computing, University of Kent, United Kingdom. Shiguang Shan, Institute of Computing Technology, Chinese Academy of Sciences, China. Shuo Wang, Chemical and Biomedical Engineering, West Virginia University, United States. Jian Liu, Centre for Systems Neuroscience, University of Leicester, United Kingdom.

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Call for applications - 2019 Nengo Summer School
by Peter Blouw Nov. 23, 2018

Nov. 23, 2018

[All details about this school can be found online at https://www.nengo.ai/summerschool] The Centre for Theoretical Neuroscience at the University of Waterloo is excited to announce our 6th annual Nengo summer school on large-scale brain modelling and neuromorphic computing. This two-week school will teach participants to use the Nengo simulation package to build state-of-the-art cognitive and neural models to run both in simulation and on neuromorphic hardware. Summer school participants will be given on-site access to Loihi, Intel’s new neuromorphic research chip [1], and will learn to run high-level applications on Loihi using Nengo! More generally, Nengo provides users with a versatile and powerful environment for designing cognitive and neural systems, and has been used to build what is currently the world's largest functional brain model, Spaun [2], which includes spiking deep learning, reinforcement learning, adaptive motor control, and cognitive control networks. For a look at last year's summer school, check out this short video: https://www.youtube.com/watch?v=NwtYgBB2N6I We welcome applications from all interested graduate students, postdocs, professors, and industry professionals with a relevant background. [1] Davies, et al. (2018). Loihi: A neuromorphic manycore processor with on-chip learning. IEEE Micro. Vol. 38 no. 1 pp. 82-99. [ https://ieeexplore.ieee.org/document/8259423] [2] Eliasmith, C., Stewart T. C., Choo X., Bekolay T., DeWolf T., Tang Y., Rasmussen, D. (2012). A large-scale model of the functioning brain. Science. Vol. 338 no. 6111 pp. 1202-1205. DOI: 10.1126/science.1225266. [ http://compneuro.uwaterloo.ca/files/publications/eliasmith.2012.pdf] ***Application Deadline: February 15, 2019*** Format: A combination of tutorials and project-based work. Participants are encouraged to bring their own ideas for projects, which may focus on testing hypotheses, modeling neural or cognitive data, implementing specific behavioural functions with neurons, expanding past models, or providing a proof-of-concept of various neural mechanisms. Hands-on tutorials, work on individual or group projects, and talks from invited faculty members will make up the bulk of day-to-day activities. A project demonstration event will be held on the last day of the school, with prizes for strong projects! Participants will have the opportunity to learn how to: - interface Nengo with neuromorphic hardware (e.g. Loihi, SpiNNaker) - build perceptual, motor, and cognitive models using spiking neurons - model anatomical, electrophysiological, cognitive, and behavioural data - use a variety of single cell models within a large-scale model - integrate machine learning methods into biologically oriented models - interface Nengo with cameras and robotic systems - implement modern nonlinear control methods in neural models - and much more… Date and Location: June 9th to June 21st, 2019 at the University of Waterloo, Ontario, Canada. Applications: Please visit http://www.nengo.ai/summerschool, where you can find more information regarding costs, travel, lodging, along with an application form listing required materials. If you have any questions about the school or the application process, please contact Peter Blouw (peter.blouw(a)appliedbrainresearch.com) The school is also partly supported by ONR and ABR, Inc. We look forward to hearing from you!

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Postdoc position at The Krembil Centre for Neuroinformatics
by Leon French Nov. 21, 2018

Nov. 21, 2018

The Computational Neurobiology Laboratory at The Krembil Centre for Neuroinformatics seeks a postdoctoral fellow to join our efforts to understand brain disorders. We focus on mining datasets of connectivity, gene expression, and epigenetics across the genome and the brain. We employ multifaceted approaches that gain information by using data that crosses scales, modalities, and species. We seek one postdoctoral fellow interested in bioinformatics and neuroscience. The primary project will be to join large transcriptomic resources with the polygenic signal of major depression to gain insight into the disorder. In addition to this project, there would be opportunities to pursue other research interests. Some of our frequently used keywords give a good description of our work: neuroinformatics, molecular neuroanatomy, genetics, transcriptomics, machine learning, text mining, neuroimaging, software development, and connectomics. The ideal candidate should have a PhD in computational biology, bioinformatics, computer science or related field with extensive knowledge in genome wide data integration. Significant interest in neuroscience and experience working with large datasets are requested. Familiarity with The Library of Integrative Network-based Cellular Signatures (LINCS) is a plus. Preferred qualifications and experience: - Excellent written and verbal communication skills - Extensive experience in programming and scripting (e.g., R or Python) - Expertise in genomics, bioinformatics, computational biology, neuroscience or related areas - Experience in analyzing, visualizing and integrating large-scale biological data - Contributions to public software projects Writing manuscripts and applying for independent grants and fellowships will be strongly encouraged. Highly motivated individuals (Ph.D. and/or M.D.) should submit a curriculum vitae, names of 3 references and a cover letter explaining their interest, and a brief description of research experience to leonfrench+recruit at gmail.com. You will support a healthy workplace that embraces diversity, encourages teamwork and complies with all applicable and regulatory requirements. This position will be located at 250 College Street, Toronto.

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2 years postdoc position in reinforcement learning-based modeling of animal behavior
by david robbe Nov. 21, 2018

Nov. 21, 2018

Dear Colleagues, Please find below an announcement for a postdoc position in Marseille (France) to develop reinforcement learning-based models of animal behavior. The Turing Centre for Living Systems in Marseille seeks applicants for a postdoc position in computational neuroscience/robotics under the joint supervision of David Robbe and Christophe Eloy. The initial appointment will be for 2 years. The goal of this project is to understand, at an algorithmic level, how animals learn to develop adaptive behavior by trial-and-error interaction with their environment. We propose an interdisciplinary approach combining behavioral experiments in rats and the development of an "artificial" agent facing challenges similar to those experienced by the rats. The experimental data (already available) show that rats challenged in a time estimation task converged progressively toward a conserved embodied strategy (Rueda-Orozco and Robbe, Nature Neuroscience, 2015). Key determinants of that strategy have been isolated experimentally, by altering task rules and environment and quantifying how these alterations affect animal performance. Learning theoretical models will be based on reinforcement learning and more recent developments coming from artificial intelligence (deep learning). The main task of the project will be to develop computational model and therefore we expect the candidates to have a solid background in the gerenal field of machine learning. The successful applicant will work in close collaboration with experimentalists and robotic engineers. We are therefore looking for an enthusiastic applicant with excellent communication skills and a track record in interdisciplinary collaborative environments Deadline for application: 1st of December 2018 (late application will be considered until the end of the year) More information and application.<http://centuri-livingsystems.org/pdp2018-11/>http://centuri-livingsystems.org/pdp2018-11/ -- David Robbe, PhD Neural Bases of Sensorimotor learning Institut de Neurobiologie de la Méditerranée (INMED) INSERM-UMR1249 163, avenue de Luminy. BP13 13273 Marseille Cedex 9 France Tel: +33 4 91 82 81 99 Fax: +33 4 91 82 81 05 Team Web Page <http://www.inmed.fr/en/en-avenir-dynamiques-neuronales-et-fonctions-des-gan…> /"It is disturbingly common for studies to include behavior as simply a hasty add-on in papers that are otherwise crammed full of multiple techniques, types of results, and even species. It is as if every paper needs to be a methodological decathlon in order to be considered important."/ Krakauer et al., Neuron 2017 <https://www.cell.com/neuron/abstract/S0896-6273(16)31040-6>

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