The Physiological Acoustics Lab (http://escabilab.uconn.edu)
at the University of Connecticut seeks applicants for a postdoctoral position in systems and computational neuroscience.
DUTIES AND RESPONSIBILITIES
We are seeking highly motivated and creative applicants who can lead a project on Misophonia, a psychological and behavioral disorder in which individuals experience adverse emotional reactions to specific sounds (“auditory triggers”).
The broad project goal is to develop computational models of hearing and perception that are fitted to human perceptual measurements and that can be used to characterize and diagnose misophonia. The models will also be used to develop sound processing tools
to remove and de-emphasize auditory triggers (e.g., with headphones) and improve participant outcomes. The project will use a variety of approaches, including human perceptual experiments, machine learning, digital signal processing, and computational models
of hearing.
UConn has a vibrant neuroscience community, and there are opportunities for collaboration between departments, institutes, and Universities involved in the project. The primary appointment is in Biomedical Engineering and/or
Electrical Engineering. The work will be conducted in collaboration with the Behavioral Neuroscience program in the Psychological Sciences department (Ian Stevenson), the Electrical and Computer Engineering/Biomedical Engineering departments at the University
of Connecticut (Monty Escabí), and the Connecticut Institute for Brain and Cognitive Sciences.
MINIMUM QUALIFICATIONS
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A PhD in Neuroscience, Biomedical Engineering, Computational Neuroscience, or a related field is required.
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Experience with experimental design, data analysis, and/or modeling.
PREFERRED QUALIFICATIONS
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Previous experience, education, or general proven knowledge in hearing or speech and language sciences.
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Interdisciplinary research experience in computational and systems neuroscience with prior research experience in human behavior, animal behavior and/or brain physiology.
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A strong analytic background, particularly in modeling neural systems and analyzing large datasets.
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Experience with acoustic signal processing, sound recognition, and machine learning.
APPOINTMENT TERMS
This is a full-time, 12-month position funded through the Misophonia Research Fund on a yearly basis. Salaries follow the post-doctoral scale and are based on experience.
TERMS AND CONDITIONS OF EMPLOYMENT
Employment of the successful candidate is contingent upon the successful completion of a pre-employment criminal background check.
TO APPLY
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a resume (including past research experience and published work),
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a one-page statement of prior research experience,
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a one-page statement of future research interests and objectives, and
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the names and contact information of
at least two individuals who can provide reference letters.
All members of the University of Connecticut are expected to exhibit appreciation of, and contribute to, an inclusive, respectful, and diverse environment for the University community.
The University of Connecticut aspires to create a community built on collaboration and belonging and has actively sought to create an inclusive culture within the workforce. The success of the University is dependent on the
willingness of our diverse employee and student populations to share their rich perspectives and backgrounds in a respectful manner. This makes it essential for each member of our community to feel secure and welcomed and to thoroughly understand and believe
that their ideas are respected by all. We strongly respect each individual employee’s unique experiences and perspectives and encourage all members of the community to do the same. All applicants will receive consideration for employment without regard to
race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.
The University of Connecticut is an AA/EEO Employer.
Ian Stevenson
Associate Professor
University of Connecticut
Department of Psychological Sciences