- Comp-neuro - lists.cnsorg.org

Tenured Junior Research/Teaching Faculty Position in Computational Neuroscience at Ecole Normale Superieure in Paris, France
by Boris Gutkin July 8, 2016

July 8, 2016

Tenured Junior Research/Teaching Faculty Position in Computational Neuroscience at Ecole Normale Superieure in Paris, France A tenured junior faculty position is expected to open in the Department of Cognitive Studies (DEC) at the ENS-Paris. The position profile is broad, focusing on computational neuroscience. The recruited candidate will join the Group for Neural Theory, which is part of the Laboratory for Cognitive Neuroscience,DEC, ENS Paris. The candidate is expected to have a strong background in theoretical neuroscience and/or computational neuroscience. The candidate is expected to develop an active research program and integrate into the GNT. Teaching in neuroscience and computational neuroscience shall be done at the ENS within the cognitive sciences masters program. The Laboratory for Cognitive Neuroscience provides opportunities to develop a collaborative research program in computational neuroscience of decision making. Further collaborative opportunities exist within the department and local laboratories involved in electrophysiology and/or psychophysics. The next cycle of recruitment for a Junior Faculty position in computational neuroscience shall be announced in the spring of 2017. In order to apply for the position candidates need to pre-qualify with the National University Council. The deadline for the pre-qualification is in October 2016. Help with putting together the application shall be given to potential candidates who should contact mc.comp.gnt(a)gmail.com

1 0

Special Issue of Applied Soft Computing on Non-iterative Approaches in Learning
by Teng Teck Hou July 8, 2016

July 8, 2016

Special Issue of Applied Soft Computing (Elsevier) (Impact Factor: 2.8) Submission is open now Special Issue on Non-iterative Approaches in Learning (Includes comparisons with iterative methods) Call for Papers http://www.journals.elsevier.com/applied-soft-computing/call-for-papers/spec ial-issue-on-non-iterative-approaches-in-learning-includ/ Optimization, which plays a central role in learning, has received considerable attention from academics, researchers, and domain workers. Many optimization problems in machine learning are solved by iterative methods which generate a sequence of improving approximated solutions with some termination criteria. These methods usually suffer from low convergence rate and are sensitive to parameter settings (such as learning rate/step size, maximum number of iterations). On the other hand, non-iterative solutions, which are usually presented in closed-form manner, are in general computationally faster than iterative solutions. However, comparative studies with iterative methods are also welcome. The main focus of this special issue is to present the recent advances in non-iterative solutions in learning. Original contributions and surveys are welcome. The special issue aims to promote non-iterative concepts in the field of learning. Even though non-iterative methods have attracted much attention in recent years, there exists a performance gap when compared with older methods and other competing paradigms. This special issue aims to bridge this gap. Besides the dissemination of the latest research results on non-iterative algorithms, it is also expected that this special issue will cover some industrial applications, present some new ideas and identify directions for future studies. The topics of the special issue include, but are not limited to: * Methods with and without randomization * Regression, classification and time series * Kernel methods such as kernel ridge regression, kernel adaptive filters, etc. * Feedforward, recurrent, multilayer, deep and other structures. * Ensemble learning * Moore-Penrose pseudo inverse, SVD and other solution procedures. * Non-iterative methods for large-scale problems with and without kernels * Theoretical analysis of non-iterative methods * Comparative studies with competing iterative methods * Applications of non-iterative solutions in domains such as power systems, biomedical, finance, signal processing, big data and all other areas Submission format and Guidelines Papers will be evaluated based on their originality, presentation, relevance and contribution to the development of non-iterative methods, as well as their suitability and the quality in terms of both technical contribution and writing. The submitted papers must be written in good English and describe original research which has not been published nor is currently under review by other journals or conferences. If used, the previously published conference papers should be clearly identified by the authors (at the submission stage) and an explanation should be provided how such papers have been extended to be considered for this special issue. Guest Editors will make an initial determination on the suitability and scope of all submissions. Papers that either lack originality, clarity in presentation or fall outside the scope of the special issue will not be sent for review and the authors will be promptly informed of such cases. Author guidelines for preparation of manuscript can be found at http://www.journals.elsevier.com/applied-soft-computing/ Manuscripts should be submitted online at: http://ees.elsevier.com/asoc/ Applied Soft Computing Journal is well indexed. Its impact factors are 2.8 (2 years) and 3.2 (5 years). Important dates Manuscript submission: 15th Aug 2016 Revised version submission: 31st Jan 2017 Acceptance notification: 31st March 2017 Expected Publication: Mid 2017 Guest Editors Dr P N Suganthan, Nanyang Technological University, Singapore. epnsugan(a)ntu.edu.sg Prof. Sushmita Mitra, Indian Statistical Institute, India. sushmita(a)isical.ac.in Dr Ivan Tyukin, Department of Mathematics, University of Leicester, UK. I.Tyukin(a)le.ac.uk

1 0

IEEE CIDM 2016
by Friedhelm Schwenker July 6, 2016

July 6, 2016

CALL FOR PAPERS Paper Submission Deadline: July 18, 2016 IEEE CIDM 2016 IEEE Symposium on Computational Intelligence and Data Mining December 6-9, 2016, Athens, Greece SSCI 2016:http://ssci2016.cs.surrey.ac.uk/CIDM.htm CIDM 2016:http://neuro.informatik.uni-ulm.de/CIDM2016/ IEEE CIDM 2016 organized by the IEEE Computational Intelligence Society Data Mining Technical Committee is part (one of 30 symposia) of the IEEE Symposium Series of Computational Intelligence (IEEE SSCI 2016). IEEE CIDM 2016 will bring together researchers and practitioners from around the world to discuss the latest advances in the field of computational intelligence applied to data mining and will act as a major forum for the presentation of recent results in theory, algorithms, systems and applications. Topics related to all aspects of data mining, such as theories, algorithms, systems and applications, particularly those based on computational intelligence technologies, are welcome; these include, but are not limited to: • Neural networks for data mining • Evolutionary algorithms for data mining • Fuzzy sets for data mining • Data mining with soft computing • Foundations of data mining • Mining with big data • Classification, Clustering, Regression • Association • Feature learning and feature engineering • Machine learning algorithms • Multimedia data mining • Text mining • Link and graph mining • Social media mining • Collaborative filtering • Crowd sourcing • Personalization • Security, privacy and social impact of data mining • Data mining applications Important Dates • Paper submission: July 18, 2016 • Notification to authors: September 12, 2016 • Final submission: October 10, 2016 • Early registration: October 10, 2016 Co-Chairs Carlotta Domeniconi, Georg Mason University, USA Friedhelm Schwenker, Ulm University, Germany Zhi-Hua Zhou, Nanjing University, China -- PD Dr. Friedhelm Schwenker University of Ulm Institute of Neural Information Processing D-89069 Ulm, Germany phone: +49-731-50-24159 fax: +49-731-50-24156 email: friedhelm.schwenker(a)uni-ulm.de www: http://www.uni-ulm.de/in/neuroinformatik/mitarbeiter/f-schwenker.html

1 0

Postdoctoral position in Computational Neuroscience at IIT (Italy)
by Stefano Panzeri July 5, 2016

July 5, 2016

(apologies for cross posting) We are seeking candidates for a postdoctoral position in computational neuroscience in the Neural Computation Laboratory at the IIT https://www.iit.it/research/lines/neural-computation directed by Stefano Panzeri and located in Rovereto (Trento), Italy. This laboratory aims to crack the neural code using neural network modeling and computational analyses of neurophysiological experiments. The laboratory offers a range of strongly interdisciplinary expertise in computational neuroscience, including information theory and other types of advanced single-trial neural analyses, advanced spectral analysis, and biophysically plausible neural network modeling. The successful candidate will develop and use tools for the analysis of large-scale simultaneous recordings of the spiking activity of cells in the cortex to understand how populations of neurons carry information. The successful candidate would have the opportunity to benefit from an extensive set of existing collaborations with leading neurophysiological laboratories, including those of Nikos Logothetis at Max Planck Tubingen (collaborating on oscillations, sensory information coding and neuromodulation), Tommaso Fellin at IIT (collaborating on Optogenetics, Neurophysiology and Neural coding), Mathew Diamond at SISSA (collaborating on the behavioral impact of information carried by spike timing), and Christopher Harvey at Harvard Medical School (collaborating on neural population dynamics and decision making). We are willing to consider candidates interested in any of the aspects of neural coding of interest to the laboratory. Candidates must hold a PhD in a numerate scientific discipline. They must be highly motivated and creative individuals who want to work in a dynamic, multi-disciplinary research environment and be willing to interact with both experimental and theoretical neuroscientists. For reference to recent work of the lab please see Safaai, H., et al (2015) Modelling the effect of Locus Coeruleous firing on single trial cortical state dynamics and sensory processing. Proc. Natl. Acad. Sci. USA 112(41): 12834–12839 Zuo Y, et al (2015) Spike timing and spike rate make complementary contributions to perceptual decisions in rat S1 and S2 cortex. Current Biology 25: 357-363 Besserve M, Lowe S, Logothetis NK, Scholkopf B, Panzeri S (2015) Shifts of gamma phase across primary visual cortical sites reflect dynamic stimulus- modulated information transfer. PLoS Biology 13(9): e1002257. doi:10.1371/journal.pbio.1002257 Panzeri, S, et al (2015) Neural population coding: combining insights from microscopic and mass signals, Trends in Cognitive Sciences 19: 162-172. Interested candidates should contact Dr. Stefano Panzeri ( stefano.panzeri(a)iit.it <https://mail.iit.it/owa/redir.aspx?C=oajclq8rPE6FcMCJ-lI1Hsu55NFH69II9YBluB…>), emailing their CV, a brief statement of research interests, and names and email addresses of 2 or 3 referees.

1 0

Associate Professor Position in Data Sciences (Big Data) at the Norwegian University of Life Sciences
by Hans Ekkehard Plesser July 5, 2016

July 5, 2016

Dear Colleagues! The Department of Mathematical Sciences and Technology (IMT) at the Norwegian University of Life Sciences (NMBU) has a permanent, full-time position available for an Associate Professor in Data Sciences (Big Data). The position is part of the University's programme for the recruitment of top researchers, which aims to recruit young researchers with great potential for NMBU. The position will include: • one PhD Position (3 years) • NOK 0.25 million in operating funds Application deadline is 1 October 2016. For details, please see https://www.jobbnorge.no/en/available-jobs/job/127472/associate-professor-i… Best, Hans Ekkehard Plesser -- Dr. Hans Ekkehard Plesser, Associate Professor Section Head Dept. of Mathematical Sciences and Technology Norwegian University of Life Sciences PO Box 5003, 1432 Aas, Norway Phone +47 6723 1560 Email hans.ekkehard.plesser(a)nmbu.no Home http://arken.umb.no/~plesse

1 0

2 open PhD positions at TU Dresden, Germany
by Stefan Kiebel July 5, 2016

July 5, 2016

The Department of Psychology at the TU Dresden (Chair of Neuroimaging, PI: Stefan Kiebel) is looking for two PhD students. One of the PhD student positions is in experimental cognitive neuroscience, and one is in computational neuroscience. Both PhD positions will be working at the interface between computational and experimental neuroimaging research in cognitive control and goal-directed action sequences. Detailed information about both positions can be found at: http://tiny.cc/8ii4by Both positions are part of the DFG-funded CRC 940 'volition and cognitive control' (http://www.sfb940.de/en/home.html) The CRC was established in 2012 and will be funded for the next four years. The CRC aims at identifying the cognitive and neuronal mechanism, which underlie the volitional control of goal-directed actions as well as its dysfunctions in selected mental disorders. PhD position 1: The candidate will work as a member of a team in the CRC940-Project A9 on computational modelling of goal-directed actions and explaining dynamics in cognitive control in terms of variations in meta-control parameters. In this project, neuroimaging (fMRI and EEG) experiments will be used to test hypotheses derived using advanced Bayesian models of goal-reaching over multiple trials. http://tiny.cc/8ii4by (PhD position Cognitive Neuroscience) PhD position 2: The candidate will work as a member of a team in the CRC940-Project Z2 on advanced computational modelling of functional magnetic resonance imaging and electroencephalography data. In this project, advanced modelling techniques will be developed and extended to enable testing novel experimental hypotheses using fMRI and EEG. http://tiny.cc/8ii4by (PhD position Computational Neuroscience) --> The application deadline for both positions is the 14th of July. -- Prof. Dr. Stefan Kiebel Technische Universität Dresden School of Science Department of Psychology 01062 Dresden, Germany Tel.: +49 (351) 463-43145 Fax: +49 (351) 463-43147

1 0

Research Engineer position - Axion/Columbia
by Robert Butera July 1, 2016

July 1, 2016

Passing this on for a colleague - contact the email address in the job ad for more information. RESEARCH ENGINEER Axion BioSystems, Inc. is a life science company located in Atlanta, GA. We design, develop and manufacture instrumentation used for the precise monitoring and control of excitable cells. Axion’s products are used by pharmaceutical and biotech companies, governmental agencies, and universities for drug safety, drug discovery, and disease modeling assays. We are currently seeking an energetic scientist to lead Axion’s efforts in a collaboration with the Institute for Genomic Medicine (IGM) at Columbia University. The objective of the collaboration is to interrogate the genetic mechanisms of epilepsy with multiwell microelectode array (MEA) technology. The newly developed IGM is an integral part of Columbia University’s Precision Medicine Initiative. As part of this Initiative, the IGM performs comprehensive genomic analyses on more than 10,000 patient genomes per year in order to identify new therapeutic targets of neurodevelopmental disorders including epilepsy. In order to understand how the implicated mutation affects disease, the laboratories of David Goldstein and Michael Boland use both primary neurons from mouse models of epilepsy and neurons derived from human induced pluripotent stem cells to study the effects of epilepsy gene mutations on neurodevelopment and neural network behavior. The selected individual will contribute analytical and electrophysiological expertise to complement a highly skilled team of molecular biologists. JOB FUNCTIONS: • Execution of neurophysiological experiments with the multiwell MEA platform. The ability to interpret or debug experiments in real-time is important, such as distinguishing signal from noise or artifacts. • Analysis of large electrophysiological data sets, primarily from the multiwell MEA platform, but also including complementary in vivo data. • Develop experimental hypotheses and identify expected results. Must confirm results through independent analysis and investigate any discrepancies that appear, while understanding the strengths and limitations of model systems (i.e., cellular vs. animal). • Drive innovation of the multiwell MEA assay, leading to new experimental methods and analytical techniques. • Work well in a fast-paced, collaborative environment at Columbia University in New York City, with an ability to communicate well with the Axion team in Atlanta. REQUIREMENTS: • PhD in neuroscience or neuroengineering. A recent post-doc experience is a plus. • Hands-on research experience with electrophysiology techniques. Specific experience with MEA technology is desired. • Biostatistical and data analysis experience. • Experience with software analysis tools such as MATLAB or MiniTAB. • Attention to detail with good documentation and communication skills. • U.S Citizenship or permanent residency required. USEFUL KNOWLEDGE: The following are not required, but will enhance an applicant’s candidacy. • Hands on experience in general biological protocols including knowledge of primary cell dissociation, cell culture protocols, CRISPR, gene editing, and basic molecular techniques. • Experience with screening technologies, such as high-throughput patch clamp or high content imaging. • Knowledge of pharmacology and neurotoxicity research. CONTACT INFORMATION: If you are interested in this position, please send your resume and cover letter to careers(a)axionbio.com

1 0

CFP: Advances in Biologically Inspired Reservoir Computing
by John Butcher July 1, 2016

July 1, 2016

Dear all, We are seeking papers for a special issue on Reservoir Computing, paper submission deadline is 31st September 2016. For more details see below and http://ispac.diet.uniroma1.it/cognitive-computation-special-issue/ Many thanks *Scope and motivation* *Reservoir computing is a family of techniques for training and analyzing recurrent neural networks, wherein the recurrent portion of the network is assigned before the training process, typically via stochastic assignment of its weights. The non-linear reservoir acts as a high-dimensional kernel space, which generates complex dynamics characterized by sharp transitions between ordered and chaotic regimes. The behavior of this model emulates the functioning of many biological (complex) systems, among which the brain.* *Driven by the conceptual simplicity of the reservoir and by links with neuroscience, computer science and systems’ theory, researchers have achieved remarkable breakthroughs, both in theory and in practice. These include dynamical models for explaining the working behavior of reservoirs, unsupervised strategies for the adaptation of the network, and the design of unconventional computing architectures for its execution.* *The recent upsurge of interest in fully adaptable recurrent networks, far from shifting the attention from the field, has brought renewed interest in reservoir computing models. In our era of extreme computational power and sophisticated problems, it is essential to understand the limits and the potentialities of simple (both deterministic and random) collections of processing units. For this reason, many fundamental questions remain open, including the design of optimal task-dependent reservoirs in a stable fashion, novel investigations on the memory and power capabilities of reservoir devices, and their applicability in an ever-increasing range of domains.* *In light of this, the aim of this special issue is to provide a unified platform for bringing forth and advancing the state-of-the-art in reservoir computing approaches. Researchers are invited to submit innovative works on the theory and implementation of this family of techniques, in order to provide an up-to-date overview on the field.* *Topics* *The topics of interest to be covered by this Special Issue include, but are not limited to:* *-Theoretical analyses on the computational power of reservoir computing.* *-Deep reservoir models.* *-Techniques for the automatic adaptation of the reservoir and the readout.* *-Supervised, unsupervised and semi-supervised training criteria.* *-Non-conventional substrates for the implementations of reservoirs.* *-Parallel and distributed algorithms for reservoir computing.* *-Comparisons between reservoir computing and standard (deep) neural networks.* *-Reservoir computing for reinforcement learning problems.* *-Fundamental links between reservoir computing and neuroscientific findings.* *-Investigation of reservoir dynamic in a phase space of reduced dimensionality.* *-Applicative papers in all areas (including robotics, industrial control, etc.) are welcome, as well as outstanding surveys on specific aspects of the field.* -- Dr John Butcher Neuroscience Research Associate School of Life Sciences Huxley Building Keele University UK ST5 5BG http://www.keele.ac.uk/lifesci/people/researchassistants/johnbutcher/

1 0

Postdoctoral Scholar in memory neuroscience using computational/modelling approach
by Sze Chai Kwok July 1, 2016

July 1, 2016

Dear all, I am looking to employ a post-doc in memory neuroscience using computational/modelling approach <http://icn.ecnu.edu.cn/Data/View/1066>. Please spread the word to interested parties. Thank you. *Postdoctoral Scholar in **memory neuroscience**using computational/modelling approach * ** A postdoctoral position is available in the Laboratory of Attention and Memory (PI: Sze Chai Kwok) in the Institute of Cognitive Neuroscience at East China Normal University (ECNU) in Shanghai, China. Research will study the theoretical and neurobiological basis of episodic memory. Focus will be on the development of cognitive and/or biophysical network models using cross-species data from both human fMRI (with TMS) and ECoG, extra-cellular LFP / whole-cell recordings (combined with ire/reversible intervention) in awake monkeys performing analogous memory tasks using naturalistic stimuli such as videos. Candidates are expected to take advantage of the Lab’s unique experimental expertise in both humans and macaques. The Lab has active collaboration with groups in UCL, Lyon Neuroscience Research Center, UCLA, and University of Oxford. Appointment is contingent upon completion of a PhD in computer science, mathematics, physics or other quantitative disciplines. Relevant research experience in modelling would be preferable. Salary is commensurate with experience. This postdoc appointment will run for initially a period of two years with a flexible starting date. Interested candidates should initiate informal inquiries and/or send a CV, a statement of research interests, representative first-author publications (in one single PDF), and references to: sk5899(a)nyu.edu <mailto:sk5899@nyu.edu>. Applications are accepted beginning now, and will continue until the position is filled. Sze Chai Kwok, DPhil Associate Professor at East China Normal University Associated faculty at NYU-Shanghai University (<http://shanghai.nyu.edu/research/brain/faculty/sze-chai-kwok>http://shanghai.nyu.edu/research/brain/faculty/sze-chai-kwok) -- ------------------------------ 5x1000 PER LA NOSTRA RICERCA SANITARIA <http://www.hsantalucia.it/modules.php?name=News&file=article&sid=999> la tua firma, il nostro codice fiscale: 97138260589 <http://www.hsantalucia.it/modules.php?name=News&file=article&sid=999> ------------------------------ Le informazioni contenute in questo messaggio sono riservate e destinate esclusivamente alla/e persona/e o all'ente sopra indicati. E' vietato ai soggetti diversi dai destinatari qualsiasi uso, copia e diffusione di quanto in esso contenuto sia ai sensi dell'art. 616 del codice penale che ai sensi della L. 196/03 (privacy). Se questo messaggio Vi e' pervenuto per errore, Vi preghiamo di non leggerlo, di distruggerlo e di comunicarci l'errore via e-mail o telefonando al numero +39 06515011. The information contained in this message is confidential and is intended for the exclusive use of the person(s) or company(s) above addessed. If you are not the intended recipient(s), you are expressly prohibited from using, copying and distributing this message or any part of it in accordance with art. 616 of the criminal code and the law 196/03 (privacy). If you receive this message please don't read it but destroy it and then inform us by e-mail or by phone - number +39 06515011.

1 0

PhD positions at the CTNSC-UniFe , Ferrara
by Emma Maggiolini June 30, 2016

June 30, 2016

The Center for Translational Neurophysiology of Speech and Communication (CTNSC) at the Istituto Italiano di Tecnologia (IIT) is looking for up to 4 highly motivated, full-time PhD students to work in the core research areas of the Center. The PhD student will work in Ferrara, in an international and multidisciplinary team including biomedical engineers, biologists, computer scientists, psychologists and medical doctors. The PhD positions will cover the following core areas of CTNSC. DEADLINE: 5TH AUGUST 2016 CTNSC Info [ITA/ENG]: https://www.iit.it/centers/ctnsc-unife PhD page [ITA/ENG]: http://www.unife.it/studenti/dottorato/concorsi/selection How to participate to the selection [ITA/ENG]:http://www.unife.it/studenti/dottorato/modulistica/concorsi/32-ciclo/neurosciences.pdf * Functional investigation of innovative neural interfaces Evidences show that using electrophysiological techniques is possible to extract information about brain processes and then translate these signals into commands that could be used to recover the lost capabilities. The purpose of the project is to investigate the in-vivo performance of novel devices - both in-house developed or obtained from collaborating laboratories - with a focus on i) enhanced capability to record neural signals; ii) large charge transfer capability to enhance the stimulation performance and iii) small size to minimize inflammatory reaction and gliosis. The candidate will be involved in the development of a stable and functional interface between living neural tissue and probes in rat model. Techniques: in-vivo single-unit recording and epicortical recording , histological techniques and image analysis, microscopy techniques and data analysis. Requirements: The candidate should have a background in one or more of the following fields: biology, medicine, pharmacology or related disciplines. Programming experience is appreciated but not mandatory. Familiarity with electrophysiological and/or microscopy techniques is of advantage. Contacts: Dr. Emma Maggiolini: emma.maggiolini(a)iit.it<mailto:emma.maggiolini@iit.it> Prof. Luciano Fadiga: luciano.fadiga(a)iit.it<mailto:luciano.fadiga@iit.it> * Animal neurophysiology investigation of sensorimotor functions The discovery of mirror and canonical neurons in monkey premotor and parietal cortex motivated a number of studies on how sensorimotor transformations in the brain may support understanding of action performed by conspecifics. This still constitute a fundamental area of research which is necessary to unravel the basic mechanisms of sensorimotor sharing during social interaction. The candidate working in this core area will investigate, in animal models, the neuronal activity of different (motor and somatosensory) cortical regions, pointing out their topographic organization as well as their role during observation, planning and execution of particular motor acts. Techniques: multidisciplinary approach involving behavioral testing, electrophysiological techniques (intracortical microstimulation, single-unit recording, field potentials recording, electromyography) and histological evaluations. Requirements: The candidate must have a background in the biological fields (biology, medicine, pharmacy and similar). At the end of the three-year period, the Ph.D. student will obtain deep knowledge and experience on in vivo neuroscience. Contacts: Dr. Riccardo Viaro: riccardo.viaro(a)iit.it<mailto:riccardo.viaro@iit.it> Prof. Luciano Fadiga: luciano.fadiga(a)iit.it<mailto:luciano.fadiga@iit.it> * Brain- and multimodal signal- based speech recognition Our approach to speech recognition is an "analysis by synthesis" approach which assumes that in order to recognize speech we need to understand the causal process (i.e., the speech production process) that stems from brain activity and, through coordinated movements of the vocal tract articulators, produces speech sounds. Such approach is largely motivated by work on speech perception carried out at CTNSC and serves two goals. The first goal is to build automatic speech recognition systems that, as humans do, work in very challenging scenarios (e.g., in the so called "cocktail party" scenario) and are able to learn how to recognize speech without relying on huge amount of carefully annotated speech data (as opposed to "data hungry" current recognition systems). The second goal is to recognize speech from one or more modalities that are within the speech production process, e.g., from brain signal or from visual and kinematic signals of the face and the vocal tract. Techniques: Machine learning, deep neural network, multimodal signal processing. Requirements: The successful candidate will have a degree in computer science bioengineering, physics or related disciplines. A background in machine learning and in speech and language processing. Contacts: Dr. Leonardo Badino: leonardo.badino(a)iit.it<mailto:leonardo.badino@iit.it> Prof. Luciano Fadiga: luciano.fadiga(a)iit.it<mailto:luciano.fadiga@iit.it> * Human Neurophysiology investigation of speech and communication abilities by means of micro-electrocorticography Historically, the study of speech processing has emphasized a strong link between auditory perceptual input and motor production output. The main aim of this area is to describe the neural systems involved in this sensorimotor representation linking speech perception and production. To this purpose, we use high-density surface microelectrodes for electrocorticography (micro-ECoG) to record electrical signals directly from the cortical surface in patients during awake surgery for low-grade glioma resection. Micro-ECoG is an important electrophysiological signal recording technique that combines high temporal resolution with good spatial localization. Techniques: Micro-ECoG, direct brain electrical stimulation, functional electrical mapping. Requirements: Applicants are expected to have a degree in biomedical engineering or basic/applied/health sciences. Requirements include a knowledge of neural signal processing and/or neuroimaging skills. Contact: Dr. Luciano Simone: luciano.simone(a)unife.it<mailto:luciano.simone@unife.it> Prof. Luciano Fadiga: luciano.fadiga(a)iit.it<mailto:luciano.fadiga@iit.it> * Human Neurophysiology investigation of speech and sensorimotor communication abilities by means of non-invasive techniques The neural mechanisms underlying speech and sensorimotor communication abilities, during real-life social encounters are mostly unknown. In the classic motor control frame of reference, individuals can be conceived as proactively building models of their action and of their sensory consequence. During interaction, these sensorimotor models can be extended to the social space whereby the control signal becomes the negotiated behavior of other people. The candidate will help in mapping the brain activities responsible for the emergence of such a shared behavior, in the domain of speech (phonological level) or action (upper arm movements). These experiments will inform the next generation of biologically inspired automatic communication recognition systems, essential to augment natural human-human coordination and promote the future of efficient human-robot interaction. Techniques: Electroencephalography (EEG), Electromyography (EMG), body motion kinematics (MoCap), Transcranial Magnetic Stimulation (TMS), transcranial Direct (tDCs) and Alternate (tACs) current stimulation. Requirements: The successful candidate will have a background in neuroscience, experimental psychology, computer science or engineering. Programming skills as well as a strong interest in cognitive neuroscience are fundamental. Electroencephalographic and kinematic data analyses skills are a plus. Contact: Dr. Alessandro D'Ausilio: alessandro.dausilio(a)iit.it<mailto:alessandro.dausilio@iit.it> Prof. Luciano Fadiga: luciano.fadiga(a)iit.it<mailto:luciano.fadiga@iit.it> [Description: cid:image001.jpg@01CC9EC9.BA36CB30] Emma Maggiolini, PhD IIT@unife, Center for Translational Neurophysiology Via Fossato di Mortara 17/19, 44121 Ferrara, Italy Tel. +39 0532 455983 SKYPE ID: lilmay75 e-mail: emma.maggiolini(a)iit.it<mailto:emma.maggiolini@iit.it> www.iit.it<http://www.iit.it/>

1 0