October 2018 - Comp-neuro - lists.cnsorg.org

Creating a list of important computational predictions
by Yiota Poirazi Jan. 4, 2021

Jan. 4, 2021

Dear all, I believe you would agree that, the greatest recognition of our computational work comes with the experimental verification of our computational predictions. To highlight the most important outcomes of our theoretical work and facilitate a closer interaction with experimental labs so as to increase the chances of our predictions being experimentally tested, I would like to encourage you to nominate your favorite prediction at the following google form: https://docs.google.com/forms/d/e/1FAIpQLSeJZ2RrXJ0nZonIuyNzN0Cl gXDKDb7xTzoJOiqZTGxm9Kh7ow/viewform?c=0&w=1&usp=mail_form_link This effort started during the CNS 2017 meeting in Antwerp with the support of the OCNS Board. The resulting list will be posted on the CNS 2017 web site and disseminated in social media, as a reference for experimentalists. Thank you very much for supporting this important effort! Best wishes, Yiota Poirazi -- Panayiota Poirazi, Ph.D. Research Director Computational Biology Laboratory Institute of Molecular Biology and Biotechnology (IMBB) Foundation of Research and Technology-Hellas (FORTH) Vassilika Vouton, P.O.Box 1385, GR 70013, Heraklion, Crete GREECE Tel: +30 2810 391139 Fax: +30 2810 391101 Εmail: poirazi(a)imbb.forth.gr Lab site: www.dendrites.gr FENS-Kavli Network of Excellence <http://www.fens.org/Outreach/FENS-Kavli-Network-of-Excellence/> -- This message has been scanned for viruses and dangerous content by *E.F.A. Project* <http://www.efa-project.org>, and is believed to be clean. Click here to report this message as spam. <http://mailgate1.imbb.forth.gr/cgi-bin/learn-msg.cgi?id=4BCD7A00A1.A57D7&to…> -- Panayiota Poirazi, Ph.D. Research Director Computational Biology Laboratory Institute of Molecular Biology and Biotechnology (IMBB) Foundation of Research and Technology-Hellas (FORTH) Vassilika Vouton, P.O.Box 1385, GR 70013, Heraklion, Crete GREECE Tel: +30 2810 391139 Fax: +30 2810 391101 Εmail: poirazi(a)imbb.forth.gr Lab site: www.dendrites.gr FENS-Kavli Network of Excellence <http://www.fens.org/Outreach/FENS-Kavli-Network-of-Excellence/>

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[publication and call for dialog] IEEE CIS Newsletter on Cognitive and Developmental Systems
by Pierre-Yves Oudeyer Jan. 23, 2020

Jan. 23, 2020

Dear colleagues, we are happy to announce the release of the latest issue of the IEEE CIS Newsletter on Cognitive and Developmental Systems (open access). This is a biannual newsletter addressing the sciences of developmental and cognitive processes in natural and artificial organisms, from humans to robots, at the crossroads of cognitive science, developmental psychology, artificial intelligence, machine learning and neuroscience. It is available at: https://goo.gl/NAwBfD Featuring dialog: === "Curiosity as Driver of Extreme Specialization in Humans" == Dialog initiated by Celeste Kidd with responses from: Elizabeth Bonawitz, Maya Zhe Wang, Brian Sweis, Benjamin Hayden, Susan Engel, Abigail Hsiung, Shabnam Hakimi, Alison Adcock, Moritz Daum, Arjun Shankar, Tobias Hauser, Goren Gordon and Perry Zurn == Topic: Curiosity-driven learning is probably one of the most fundamental mechanisms in human learning, and yet it is also probably one of the least understood. Broadly construed as spontaneous exploration and engagement with activities or material without any extrinsic goal (as opposed to searching for information useful for an extrinsic goal), many mysteries remain to be uncovered. What are the causal links between curiosity and learning? How does prior knowledge about a topic or an activity relates to curiosity about this topic? What is the role of curiosity in life-span development? Can human curiosity explain the apparently unique tendency of humans for extreme specialization? Reversely, how do different forms of curiosity (diversive or specific) evolve as children grow up and become adults? While early computational models of curiosity propose theoretical approaches to understand their cognitive mechanisms, how can we understand the affective/ emotional dimensions of curiosity? And how has the linguistic concept of “curiosity” evolved in occidental culture? Call for new dialog: === « Leveraging Adaptive Games to Learn How to Help Children Learn Effectively" == Dialog initiated by George Kachergis == Topic: How can one achieve efficiently “translational educational sciences” and get these principles used in real-world large-scale educational technologies? In this dialog, Georges Kachergis highlights challenges related to collaborations between cognitive scientists and game developers, how to deploy real world experiments, and how to enable scientific understanding when many variables cannot easily be controlled? Those of you interested in reacting to this dialog initiation are welcome to submit a response by December 15th, 2018. The length of each response must be between 600 and 800 words including references (contact pierre-yves.oudeyer(a)inria.fr) Let us remind you that all issues of the newsletter are all open-access and available at: https://goo.gl/ZjjZNz I wish you a stimulating reading! Best regards, Pierre-Yves Oudeyer, Editor of the IEEE CIS Newsletter on Cognitive and Developmental Systems Research director, Inria Head of Flowers project-team Inria and Ensta ParisTech, France http://www.pyoudeyer.com Twitter: https://twitter.com/pyoudeyer and Fabien Benureau, Editorial Assistant Cognitive NeuroRobotics Unit, Okinawa Institute of Science and Technology 1919-1 Tancha, Onna, Okinawa Japan Email: fabien.benureau [at] oist [dot] jp

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Postdoc positions on Consciousness Research at Monash Uni, Australia
by Naotsugu Tsuchiya Jan. 14, 2020

Jan. 14, 2020

Position A: DP180100396 (in collaboration with Masafumi Oizumi, Hiroto Kawasaki, Giulio Tononi) No report paradigm in EEG/MEG/intracranial recordings and the analysis of integrated information across sensory and prefrontal cortex Position B: DP180104128 (in collaboration with Marta Garrido, Ueli Rutishauser, Ralph Adolphs) The analysis of MEG/intracranial recordings, using Granger causality and integrated information We are looking for highly talented postdocs (application from PhD students with a prospect of getting PhD in 1 year are welcomed) for the above two ARC-funded positions. Ideal candidates in both positions would have strong publications, strong programming skills in Matlab/Python, strong background in data analysis, preferably neuroimaging data or neural spikes. The projects include 1) design of various psychophysical paradigms (*pdf <https://academic.oup.com/nc/article/doi/10.1093/nc/niw023/2970153/Are-we-un…>)*, including no-report paradigms (*pdf <https://drive.google.com/file/d/0B9N0ysU85rs7bEF5d1VVX3J4T3c/view>*), which can be used for subsequent EEG/MEG/intracranial recording experiments and 2) analysis of EEG/MEG/intracranial data using the approximations of integrated information (link1 <http://www.eneuro.org/content/4/5/ENEURO.0085-17.2017>, *link <https://drive.google.com/open?id=0B9N0ysU85rs7NFVSQlIza215eVU> *, *link <http://www.pnas.org/content/early/2016/12/02/1603583113.abstract>*, *link <http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.10046…>)* ). Please send the inquiry with your CV to naotsugu.tsuchiya(a)monash.edu. Applicants with most fit background and skills will be contacted for subsequent interview over the internet. The positions will be available until they are filled. Regards Nao Tsuchiya -- -- *Nao (Naotsugu) Tsuchiya* Associate Professor *Monash Institute of Cognitive and Clinical Neurosciences* Attention and Memory Program School of Psychological Sciences Monash University 770 Blackburn Monash Brain Imaging facility, Clayton, VIC 3800 Australia T: +61 3 9905 4564 E: naotsugu.tsuchiya(a)monash.edu W: homepage: http://users.monash.edu.au/~naotsugt/Tsuchiya_Labs_Homepage http://med.monash.edu.au/psych/email-sig/miccn-email-sig.jpg <http://www.monash.edu/neuro-institute/> 2. Visiting Researcher at Department of Dynamic Brain Imaging, Advanced Telecommunications Research (ATR), Japan 3. Visiting Researcher at CiNet, Osaka University, Japan orcid.org/0000-0003-4216-8701

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Professorship in Artificial Intelligence
by Steuber, Volker May 8, 2019

May 8, 2019

Professor of Artificial Intelligence University of Hertfordshire, UK School of Computer Science, College Lane Campus, Hatfield Salary £58,293 to £64,397 pa dependent on skills and experience FTE: Full time position working 37 hours per week (1.0FTE) Duration of Contract: Permanent position Closing date: 3 September 2018 Applications are invited for an exceptionally well qualified Professor of Artificial Intelligence of international standing who shares our commitment to research-informed teaching, embraces the research impact agenda, and values collegiality. Applicants will strengthen existing activity within the School and develop it further, enhancing the research strengths and external reputation of the School of Computer Science and contributing significantly to its performance in future research assessments and collaborations with public and commercial partners. The successful applicant will connect to and complement the research conducted by the Adaptive Systems Research Group within the Centre for Computer Science and Informatics Research. The School hosts the University of Hertfordshire Robot House, a facility ubiquitously equipped with low-cost sensors providing a realistic environment for experiments with robotics in a smart home; a fully-fledged RoboCup lab for development of humanoid robots participating in international competitions, and a large variety of different robotic platforms, including iCub and Baxter. We encourage applications from researchers across a wide spectrum of AI, and particularly encourage applications from researchers working on emerging topics in AI. Possible fields include, but are not limited to: • Machine learning: reinforcement learning, Deep Methods, statistical methods, large scale data modelling/intelligent processing and high-performance learning algorithms • Robotics: embodied and/or cognitive robotics, HRI, robot safety, emotional/social robots, smart homes and sensors, sensor fusion, assistive robotics • Complex Systems: collective intelligence, adaptive, autonomous and multi-agent/robot systems, collective and swarm intelligence, social and market modelling, adaptive, evolutionary and unconventional computation • Mathematical Modelling: statistical modelling, information-theoretic methods, compressive sensing, intelligent data visualization, multiscale models, optimization • Emerging Topics in AI: computer algebra and AI, topological methods (e.g. persistent homology), modern topics in games and AI • AI and applications: financial modelling, AI and biology/physics/cognitive sciences • Foundations: fundamental questions of intelligence and computation, emergence of life/intelligence, Artificial Life As well as providing research supervision at PhD and MSc level, the School of Computer Science offers modules in Artificial Intelligence, machine learning, neural and evolutionary computing, as well as Artificial Life on the BSc and MSc programmes. Applicants will be committed to a strong research and research-informed teaching agenda, and will actively contribute to increase research impact of the School, in the enthusiastic, welcoming and collegiate environment of the school. Applicants must hold a PhD (or equivalent) in a relevant subject, possess excellent communication skills in English, an internationally excellent research record and reputation, including publications of the highest quality suitable for submission to the REF (UK Research Excellence Framework). Applicants are expected to show evidence of experience and success in research/commercial income generation, engagement with the research impact agenda, engagement with key stakeholders in the relevant Artificial Intelligence community and management and leadership ability. The professor will have opportunity to contribute actively to interdisciplinary research projects at the school and university level. Applications must be made online https://www.herts.ac.uk/contact-us/jobs-and-vacancies/academic-vacancies?, reference 016194?. Informal enquiries may be addressed to Dr Volker Steuber (Associate Dean Research, v.steuber(a)herts.ac.uk) or Professor William Clocksin (Dean of School, w.clocksin(a)herts.ac.uk) Please note that applications sent directly by email will not be accepted.

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[journals] CfP : Special Issue on Continual Unsupervised Sensorimotor Learning
by Nguyen, Sao Mai March 14, 2019

March 14, 2019

Dear Colleagues, IEEE Transactions on Cognitive and Developmental Systems is currently running a Special Issue entitled " Continual Unsupervised Sensorimotor Learning". Dr. José Antonio Iglesias Martínez, Assoc. Prof. We would like to invite you to prepare a research article or a comprehensive review to be published in this special issue. AIM AND SCOPE Although machine learning algorithms continue to improve at a rapid pace enabling technologies and products such as autonomous driving cars and sophisticated image and speech recognition, it is often forgotten that these applications represent tailored solutions to specific tasks. Thus it is not clear if or how these autonomous systems can pave the road to general purpose machines envisioned by many. The pursuit for higher levels of autonomy and versatility in robotics is arguably lead by two main factors. Firstly, as we push robots out of the labs and productions lines, it becomes increasingly difficult to design for all possible scenarios that a particular robot might encounter. Secondly, the cost of designing, manufacturing, and maintaining such systems becomes prohibitive. As the algorithms for learning single tasks in restricted environments are improving, new challenges have gained relevance in order to get more autonomous artificial systems. These challenges include multi-task learning, multimodal sensorimotor learning and lifelong adaptation to injury, growth and ageing. Addressing these challenges promise higher levels of autonomy and versatility of future robots. This special issue on Continual Unsupervised Sensorimotor Learning is primarily concerned with the developmental processes involved in unsupervised sensorimotor learning in a life-long perspective, and in particular the emergence of representations of action and perception in humans and artificial agents in continual learning. These processes include action-perception cycle, active perception, continual sensory-motor learning, environmental-driven scaffolding, and intrinsic motivation. The special issue will highlight behavioural and neural data, and cognitive and developmental approaches to research in the areas of robotics, computer science, psychology, neuroscience, etc. Contributions might focus on mathematical and computational models to improve robot performance and/or attempt to unveil the underlying mechanisms that lead to continual adaptation to changing environment or embodiment and continual learning in open-ended environments. Contributions from multiple disciplines including cognitive systems, cognitive robotics, developmental and epigenetic robotics, autonomous and evolutionary robotics, social structures, multi-agent and artificial life systems, computational neuroscience, and developmental psychology, on theoretical, computational, application-oriented, and experimental studies as well as reviews in these areas are welcome. THEMES This special issue aims to report state-of-the-art approaches and recent advances on Continual Unsupervised Sensorimotor Learning with a cross-disciplinary perspective. Topics relevant to this special issue include but are not limited to: Emergence of representations via continual interaction Continual sensory-motor learning Action-perception cycle Active perception Environmental-driven scaffolding Intrinsic motivation Neural substrates, neural circuits and neural plasticity Human and animal behaviour experiments and models Reinforcement learning and deep reinforcement learning for life-long learning Multisensory robot learning Multimodal sensorimotor learning Affordance learning Prediction learning SUBMISSION Manuscripts should be prepared according to the “Information for Authors” of the journal found at http://cis.ieee.org/component/content/article/7/131-ieee-transactions-on-au…. Submissions must be done through the IEEE TCDS Manuscript center: https://mc.manuscriptcentral.com/tcds-ieee. Please select the category “SI: Continual Unsupervised Sensorimotor Learning”. IMPORTANT DATES 6th January 2019 – Paper submission deadline 15th March 2019 – Notification for authors 31st May 2019 – Deadline revised papers submission 30th June 2019 – Final notification for authors 31st July 2019 – Deadline for camera-ready versions September 2019 – Expected publication date More information on Continual Unsupervised Sensorimotor Learning http://projects.au.dk/socialrobotics/news-events/show/artikel/special-issue… GUEST EDITORS Nicolás Navarro-Gerrero Aarhus University, Aarhus, Denmark nng(a)eng.au.dk Sao Mai Nguyen IMT Atlantique, Francenguyensmai(a)gmail.com Erhan Öztop Özyeğin University, Turkeyerhan.oztop(a)ozyegin.edu.tr Junpei Zhong National Institute of Advanced Industrial Science and Technology (AIST), Japanjoni.zhong(a)aist.go.jp ---- Nguyen Sao Mai nguyensmai(a)gmail.com Researcher in Cognitive Developmental Robotics http://nguyensmai.free.fr

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Postdoctoral positions in computational neuroscience and artificial intelligence
by Maxim Bazhenov March 1, 2019

March 1, 2019

Applications are invited for post-doctoral research positions in the laboratory of Dr. Maxim Bazhenov at the University of California, San Diego to study memory and learning and to develop neuroscience inspired computer algorithms capable of continual learning and adapting to the novel situations and contexts.This project involves close collaboration with the experimental laboratory of Dr. Bruce McNaughton (UC Irvine). The ultimate goal of the work is to advance the knowledge of how human and animal brains learn from experience and apply these principles to the artificial systems to enable continuous learning without catastrophic forgetting. The successful candidate will collaborate with a team of researchers to design neural network models of dynamic interactions between the hippocampus and neocortex during learning and memory consolidation in sleep and awake based on experimental data. These models will be used to derive learning principles that can be combined with advances in artificial intelligence and machine learning. An ideal candidate should have experience in computational/theoretical neuroscience and a basic knowledge of machine learning, or, alternatively, experience in machine learning algorithms and some basic knowledge of neuroscience. Experience with hierarchical learning, reinforcement learning, and/or goal-directed decision-making would be particularly helpful. The University of California offers excellent benefits. Salary is based on research experience. Applicants should send a brief statement of research interests, a CV and the names of three references to Maxim Bazhenov at mbazhenov(a)ucsd.edu -- Maxim Bazhenov, Ph.D. Professor, Department of Medicine, Neurosciences Graduate Program, UCSD, School of Medicine http://www.bazhlab.ucsd.edu/

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Columbia Workshop on Brain Circuits, Memory and Computation 2018
by Aurel A. Lazar Dec. 16, 2018

Dec. 16, 2018

Columbia Workshop on Brain Circuits, Memory and Computation 2018 <http://www.bionet.ee.columbia.edu/workshops/bcmc/2018> BCMC 2018 March 15-16, 2018 Center for Neural Engineering and Computation <http://cnec.columbia.edu/> Columbia University, New York, NY, USA Overview The goal of the workshop is to bring together researchers interested in developing executable models of neural computation/processing of the brain of model organisms. Of interest are models of computation that consist of elementary units of processing using brain circuits and memory elements. Elementary units of computation/processing include population encoding/decoding circuits with biophysically-grounded neuron models, non-linear dendritic processors for motion detection/direction selectivity, spike processing and pattern recognition neural circuits, movement control and decision-making circuits, etc. Memory units include models of spatio-temporal memory circuits, circuit models for memory access and storage, etc. A major aim of the workshop is to explore the integration of various sensory and control circuits in higher brain centers. A Fruit Fly Brain Hackathon <http://www.bionet.ee.columbia.edu/hackathons/ffbh/2018> is being conducted in conjunction with the workshop. Workshop participants are welcome to attend the hackathon. Registration Registration is free but all participants have to register <https://bcmc2018.eventbrite.com/>. Thank you! Organizer and Program Chair Aurel A. Lazar <http://www.ee.columbia.edu/~aurel>, Department of Electrical Engineering, Columbia University. Program Overview (Confirmed Speakers) Misha B. Ahrens <https://www.janelia.org/lab/ahrens-lab>, Janelia Research Campus, Ashburn, VA. Davi Bock <https://www.janelia.org/lab/bock-lab>, Janelia Research Campus, Ashburn, VA. Claude Desplan <https://wp.nyu.edu/desplanlab/>, Department of Biology, New York University. Mark A. Frye <http://www.fryelab.net/>, Department of Integrative Biology and Physiology, University of California, Los Angeles. C. Giovanni Galizia <http://neuro.uni-konstanz.de/03team/content/galizia.html>, Department of Biology, University of Konstanz. Maria N. Geffen <https://geffenlab.weebly.com/maria.html>, Department of Otorhinolaryngology and Department of Neuroscience, University of Pennsylvania. Hokto Kazama <http://kazama.brain.riken.jp/index.php>, Riken Brain Science Institute, Tokyo. Donggen Luo <http://mgv.pku.edu.cn/?co=posts&ac=faculty&catalog=enpiintro&pname=en_Dongg…>, McGovern Institute for Brain Research, Peking University, Beijing. Thomas Nowotny <http://www.sussex.ac.uk/profiles/206151>, Department of Informatics, University of Sussex. Pavan Ramdya <https://ramdya-lab.epfl.ch/home>, Brain Mind Institute, EPFL, Lausanne. Aravinthan D.T. Samuel <https://scholar.harvard.edu/aravisamuel/home>, Department of Physics and center for Brain Science, Harvard University. Simon G. Sprecher <http://www.sprecherlab.com/simon-sprecher/>, Department of Biology, University of Fribourg. Seth Tomchik <http://www.scripps.edu/tomchik/>, The Scripps Research Institute Florida. Joshua T. Vogelstein <http://jovo.me/>, Dept. of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University. Scott Waddell <https://www.dpag.ox.ac.uk/research/waddell-group>, Department of Physiology, Anatomy and Genetics, University of Oxford. Anthony M. Zador <http://zadorlab.cshl.edu/>, Cold Spring Harbor Laboratory. Aurel http://www.bionet.ee.columbia.edu

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Postdoc position in Geometric Methods for Machine Learning and Deep Learning at RIST
by Luigi Malagò Dec. 14, 2018

Dec. 14, 2018

=========================================================== *Subject*: Postdoc position in Machine Learning (1 year, renewable up to 2 years) *Institution*: RIST - Romanian Institute of Science and Technology, Cluj-Napoca *Keywords*: Geometry of Latent Spaces, Optimization over Manifolds, Information Geometry, Natural Gradient, Riemannian Geometry *Application deadline*: 10 October 2018 (applicants are encouraged to apply earlier) *Salary*: around 2200 euro net *Official announcement*: http://rist.ro/en/details/news/postdoc-positions-in-deep-learning-and-machi… =========================================================== Dear colleagues, the Romanian Institute of Science and Technology (RIST) has an opening for a postdoc position, in the context of the DeepRiemann project “Riemannian Optimization Methods for Deep Learning”, funded by European structural funds through the Competitiveness Operational Program (POC 2014-2020). The appointments will be for 1 year, with possible extensions up to 2 years. The DeepRiemann project aims at the design and analysis of novel training algorithms for Neural Networks in Deep Learning, by applying notions of Riemannian optimization and differential geometry. The task of the training a Neural Network is studied by employing tools from Optimization over Manifolds and Information Geometry, by casting the learning process to an optimization problem defined over a statistical manifold, i.e., a set of probability distributions. The project is highly interdisciplinary, with competences spanning from Machine Learning to Optimization, Deep Learning, Statistics, and Differential Geometry. The objectives of the project are multiple and include both theoretical and applied research, together with industrial activities oriented to transfer knowledge, from the institute to a startup or spin-off of the research group. The positions will be part of the new Machine Learning and Optimization group https://rist.ro/en/teams.html, which performs research at the intersection of Machine Learning, Stochastic Optimization, Deep Learning, and Optimization over Manifolds, using geometric methods based on Information Geometry. The group is one of two newly-formed groups in Machine Learning at RIST. The official job announcement can be seen here: http://rist.ro/en/details/news/postdoc-positions-in-deep-learning-and-machi… Informal inquiries can be sent to Dr. Luigi Malagò <malago(a)rist.ro>, principal investigator of the DeepRiemann project. Application deadline: 10 October 2018 (applicants are encouraged to apply earlier) best regards, Luigi Malagò

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Postdoc Position in Neural Data Analysis, University of Edinburgh
by Arno Onken Oct. 30, 2018

Oct. 30, 2018

The School of Informatics, University of Edinburgh, invites applications for a full-time, fixed term Research Associate position on the project "Novel techniques for stochastic modelling of time-dependent multivariate relationships with application to primary visual cortex", funded by the UK Engineering and Physical Sciences Research Council (EPSRC), and directed by Dr Arno Onken (PI). The successful candidate will contribute to the development of a new framework for analysing multi-modal and multi-scale neural recordings. Moreover, the candidate will apply this framework to analyse activity of large neural populations recorded from awake mice during a virtual reality task by experimental project partner Dr Nathalie Rochefort. Candidates should have a PhD (or near completion) in a quantitative discipline (Mathematics, Computer Science, Physics, Computational Neuroscience or related), good software development skills and a keen interest in interdisciplinary research. A background in neuroscience is desirable, but not essential. The post is full time and fixed term at UE07: £33,199 - £39,609 pa. The post is available from 1st March 2019 for 36 months until the scheduled end of the project at 28th February 2022. For further information, please visit the vacancy website at: https://www.vacancies.ed.ac.uk/pls/corehrrecruit/erq_jobspec_version_4.jobs… Informal enquires can be made to Dr Arno Onken <aonken(a)inf.ed.ac.uk>. -- The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336.

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PhD in Cognitive Sciences at UC Irvine
by Jeff Krichmar Oct. 30, 2018

Oct. 30, 2018

Dear colleagues, The Department of Cognitive Sciences at UC Irvine has a number of openings for new graduate students starting Fall of 2019. We are in the process of expanding our graduate program, so please feel free to forward this information to any interested parties. All this information and more can be found via cogs.ci. Our department specializes in • Computational and mathematical cognitive modeling • Cognitive neuroscience • Visual and auditory perception • Attention and representation • Learning and development • Memory and language • Judgment and decision making ... and is generally strong in the use of leading technologies such as EEG, fMRI, and robotics, and of modern research tools such as computational methods, big data, and Bayesian statistics. We offer PhD degrees in Cognitive Sciences with an optional specialization in cognitive neuroscience and an optional joint degree (MS/PhD) in Statistics. We also take pride in offering excellent financial packages to our graduate students, including at least five years of guaranteed support, health insurance, research funding, and more. These positions are also open to international students with appropriate academic qualifications (i.e., a Bachelor's degree in a relevant field). This year's deadline to apply is December 1, 2018. We are happy to answer questions you or your students might have. Best, Joachim & Jeff -- Joachim Vandekerckhove Associate Professor Department of Cognitive Sciences Department of Statistics University of California, Irvine 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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