Wednesday, March 27, 2019

Postdoc Positions in Cognitive Neuroscience of Communication at the University of Connecticut

The Cognitive Neuroscience of Communication-CT program is funded by a T32 Institutional Research Service Award from the NIH (Inge-Marie Eigsti & Emily Myers, Program Directors). The goal of this program is to provide targeted training in the cognitive neuroscience of communication disorders to predoctoral and postdoctoral scholars.  We invite applications for two-year postdoctoral fellowships, to begin in the Fall of 2019.
Postdoctoral trainees will work under the supervision of one or more mentors on the CNC-CT team. These mentors are: Richard Aslin (Haskins Labs and University of Connecticut), Inge-Marie Eigsti, Deborah Fein, Roeland Hancock, Fumiko Hoeft, Nicole Landi, James Magnuson, Jay Rueckl (Psychological Sciences, University of Connecticut), and Emily Myers, Erika Skoe, and Rachel Theodore (Speech, Language, and Hearing Sciences, University of Connecticut).  For more information about the details of the training program, visit the program’s website (  Note that applicants must be US citizens or green card holders.
The successful candidate will join the intellectually rich community at the University of Connecticut, and will have opportunities to collaborate with an outstanding group of scientists and clinicians and to build an independent research program.
  1. PhD in a relevant field, such as Psychology, Cognitive Neuroscience, or Speech, Language, and Hearing Sciences.
  2. Evidence of research productivity.
  3. Applicants must contact a prospective mentor from the team to assess degree of fit to the program.
  1. Experience with neuroimaging and neuromodulation methods, (e.g. ERP/EEG, MEG, fMRI, fNIRS, tDCS, TMS).
  2. Experience with clinical populations affected by communication disorders (e.g. aphasia, developmental language disorder, reading disorder, hearing loss, autism).
  3. Computational skills including advanced statistical methods, coding abilities (e.g. R, Python), or computational modeling experience.
This will be a full-time, 12-month, two-year appointment based on funding availability, performance, and mutual agreement.  Salary will be commensurate with experience and consistent with NIH NRSA stipends. We anticipate a Fall 2019 start date. For additional information regarding benefits visit:  
To apply, first contact a prospective mentor from the mentorship team. If that person agrees to sponsor your application, you will work with your prospective mentor to develop a one-page research proposal detailing your planned research and training during the traineeship. Next, please apply online at, Staff Positions, Search #2019433. Submit a letter of interest including the one-page research proposal, a curriculum vitae, up to three representative publications, and the contact information for three references. Questions regarding this position may be directed to Directors Eigsti or Myers at
Employment of the successful candidate will be contingent upon the successful completion of a pre-employment criminal background check. (Search # 2019433)
This job posting is scheduled to be removed at 11:59 p.m. Eastern time on May 1, 2019.
All employees are subject to adherence to the State Code of Ethics, which may be found at

The University of Connecticut is committed to building and supporting a multicultural and diverse community of students, faculty and staff. The diversity of students, faculty and staff continues to increase, as does the number of honors students, valedictorians and salutatorians who consistently make UConn their top choice. More than 100 research centers and institutes serve the University’s teaching, research, diversity, and outreach missions, leading to UConn’s ranking as one of the nation’s top research universities. UConn’s faculty and staff are the critical link to fostering and expanding our vibrant, multicultural and diverse University community. As an Affirmative Action/Equal Employment Opportunity employer, UConn encourages applications from women, veterans, people with disabilities and members of traditionally underrepresented populations.

Tuesday, March 26, 2019

Postdoc at The Massachusetts General Hospital with David Gow

Postdoctoral Research Fellowship at The Massachusetts General Hospital

Qualified individuals are invited to apply for a postdoctoral fellow position bridging cognitive neuroscience, phonology, and language processing. This position is supported by an NIDCD R01 awarded to Dr. David Gow (Massachusetts General Hospital) and Dr. Seppo Ahlfors (Athinoula A. Martinos Center for Biomedical Imaging). The postdoctoral scholar will be stationed in Cambridge near the MIT campus, and will image at the Athinoula A. Martinos Center for Biomedical Imaging in Charlestown, MA. We are looking for a motivated and enthusiastic postdoctoral candidate with a neuroscience experience (in particular MEG) and strong coding skills who will play a key role in a project that uses neural decoding and effective connectivity analyses developed in our lab to study phonological constraints on spoken language perception.  These analyses are performed on MR-constrained sourcespace reconstructions of simultaneous MEG/EEG data collected during spoken language perception tasks. Candidates must have a Ph.D. in Neuroscience, Psychology, Linguistics, or a related field, or have completed all of the requirements for a Ph.D. by the time of appointment. Experience with sourcespace analyses of electrophysiological data is strongly preferred. The postdoctoral scholar will be expected to design and coordinate experiments using our methods, present data at national and international conferences, and write manuscripts. Extensive mentorship is available for these activities to help prepare for an independent research career. The postdoctoral scholar will have opportunities to interact with vibrant research communities at the Massachusetts General Hospital/Harvard Medical School, the Athinoula A. Martinos Center for Biomedical Imaging, and MIT. The initial appointment will be for one year, with potential extensions for 1-2 additional years. One need not be a U.S. citizen to apply. Salary and benefits are based on NIH guidelines, commensurate with experience and qualifications. Interested candidates must submit a current CV, a cover letter with a statement of research experience and interests, 2-3 recent publications, and the names and contact information for three references. Review of applications will begin immediately and continue until the position is filled. To submit an application or request  more information about this position, please contact Dr. David Gow at or 617-726-6143.

Thursday, March 14, 2019

Brainhack Donostia 2019 - 5th-8th of May 2019, BCBL, San Sebastian

Dear all,

At the Basque Center on Cognition, Brain and Language (BCBL), we are organizing the second edition of Brainhack Donostia (, an event focused on neuroscience and the promotion of open-source resources in an accessible way across disciplines and experience levels. 

This edition will take place on May 5th-8th 2019 at BCBL, where two invited speakers and other scientists from BCBL will give talks and develop hands-on tutorials on data handling from several neuroimaging techniques. In particular, we will cover three techniques: Diffusion-Weighted Imaging (DWI), Magnetoencephalography (MEG) and functional Magnetic Resonance Imaging (fMRI). To know more about it, you can check the schedule here: 
We are also going to host a hackathon in parallel, in which the attendants can propose, and collaborate on, neuroimaging-related projects (e.g., data acquisition, visualization, etc.). Check the project page for more info: 

We encourage you to come and enjoy such an experience with us! Feel free to register here

For any inquiries, please email us at

Looking forward to meet you in May!

BrainHack Donostia Organising Team

Sunday, March 3, 2019

Is there a 'there there' for neuro-'science'? Or will it be engineering? Sunday morning thoughts...

It's Sunday morning in New York, and I really need a break from grant writing... So here a few cranky thoughts from a cranky guy.
Is modern neuroscience on the right path – or showing signs of early demise?

Two of the most exciting areas of contemporary science are to understand the structure of the universe and the structure and function of the human brain. The problem, incidentally, turns out to be similar in scope. Our neighborhood galaxy, the Milky Way, is estimated to contain about 100 billion stars. The human brain is estimated to contain about 86 billion neurons. Although different in kind, the scale of these scientific questions suggests that we need experimentation and quantitative analysis over enormous numbers and astonishing complexity.

The galaxy is big - and distant. The brain is small and between our ears. But the brain may be the bigger challenge. Because of the almost perverse connectivity of cells with each other and the extremely rapid processing timescales (milliseconds), understanding how the brain is organized and in which way it forms the basis for everything we do - from regulating are heartbeat to making complex decisions to enjoying music - is clearly one of the most profound challenges for modern science. That is presumably why we like it and do it, no?

As neuroscientists, we celebrate a veritable orgy of successes, suggesting that from molecule to mind, our techniques are steadily illuminating each aspect of brain function. We capitalize on cutting-edge developments, and practically every one of our graduate students is now a literate in big data and deep learning.

This is all wonderful, and keeps us employed. Scratching on the surface however, reveals that there are deep underlying problems – and the acute danger that human brain science may be merely ‘descriptive,’ epistemologically flawed, and perhaps going down a completely dangerous and even sterile path. Indeed, it appears that in the neurosciences, "science" is being replaced by engineering... Provocative papers have begun to point out that this ever growing field is departing from approaches that have been successful, for better or for worse (e.g. Krakauer er al, 2017, Neuron).

The history of science tells us that concepts such as ‘explanation,’ ‘mechanism,’ ‘theory’ lie at the foundation of modern science since the enlightenment. 

Remarkably, contemporary neuroscience has increasingly divorced itself from the standard approaches of the sciences. This may be a good thing and legitimate and ‘epistemologically innovative’ – or it may be catastrophic and lead to the sterility of the neurosciences ... If explanation and mechanism are replaced by concepts such as prediction (“As long as my model predicts with a good fit, I am happy” prominent senior neuroscientist), if understanding is in fact no more than model-based prediction, then this exciting and growing branch of the sciences will have departed from the aims that we typically pursue in the sciences. We will continue to embrace big data - and abandon big theory.

To be sure, nobody argues against having access to large amounts of data. Whether collecting genomes, transcriptomes, connectomes, any-other-kind-of-omes, physiological recordings from hundreds of cells, or brain imaging from tens of thousands of locations, everyone realizes the value of the ever-growing body of data we can measure and analyze. There is very little risk in having more information. Perhaps the marriage of big data and relentless reductionism really is the answer to our questions.

That being said, there is a huge risk in having too few ideas, too few guiding principles, too few (warning: old school concept ahead…) “hypotheses” that provide the intellectual substrate that motivates why we do the work to begin with! And let’s be clear: just saying ”I have a computational model” that underlies my data analysis a theory not makes. In short, we are at a critical juncture in the neurosciences. We will, that goes without saying, continue to adopt the methods associated with big data and deep learning, because they are important expansions of the toolboxes we have to examine these most complex set of phenomena. But if we replace the fundamental, mechanistic, explanatory understanding of biology, brain, cognition, with data-driven correlation, regression, model fitting – when our data have become our theory – will the price be too high?

Examining these ideas -- that is to say engaging in a systematic, sustained, and perhaps countercultural and unpopular reflection on where this most important field is going -- will illuminate whether the next 30 to 50 years in the neurosciences will constitute the success of correlation over explanation, or whether there is a path forwards that recaptures the value of ideas that form the basis of fundamental understanding. With computation, broadly construed, sitting centrally at the issues facing this field, it will be critical to assess (and recommend) whether we should continue to replace scientists with engineers and whether we should abandon the Baconian ideals of how the sciences have worked for the last several hundred years.