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            <h2><span style="font-family:Calibri,sans-serif;font-size:11pt;font-weight:normal">We are seeking one highly motivated postdoctoral fellow in 
computational neuroscience and signal processing to be part of an 
interdisciplinary research alliance working to develop computational 
models and data analysis methods in support of a research program in 
neuroergonomics (‘the study of the brain and body at work’). An overall 
goal of the research is to discover underlying principles describing the
 relationship of non-invasively recorded EEG brain dynamics and 
motivated behavior (recorded by body motion capture, eye tracking, etc) 
in interactive, information-rich human-system operating environments and
 to apply these principles to support overall performance of complex 
system operations.</span></h2><div id="detailTab">
<p><span style="font-size:11pt;font-family:'Calibri','sans-serif'">The
 ideal candidate will have strong background in statistical learning, 
data analysis, and visualization with preferably research experience in 
neuroscience or cognitive science. The candidate will be based at 
University of Texas at San Antonio (UTSA) and needs to work closely with
 researchers and students at participating universities and to present 
their research at conferences and in the open research literature. </span></p>
<p><strong><span style="text-decoration:underline"><span style="font-size:11pt;font-family:'Calibri','sans-serif'">Instructions to Applicants</span></span></strong><span style="font-size:11pt;font-family:'Calibri','sans-serif'">:
 Applicants should submit a cover letter and CV, including the names and
 contact information of three references. Include in the cover letter 
accompanying the application a summary of your research experience and 
goals. Please send application materials by e-mail to:</span></p>
<p><span style="font-size:11pt;font-family:'Calibri','sans-serif'"> <span style="font-size:11pt">Yufei Huang, Associate Professor, Department of Electrical and Computer Engineering, University of Texas at San Antonio; <a href="mailto:yhuang@utsa.edu">yufei.huang@utsa.edu</a>; <a href="http://engineering.utsa.edu/~yfhuang/">http://engineering.utsa.edu/~yfhuang/</a>; <span class="skype_pnh_print_container_1337713698">(210) 458-6270</span><span class="skype_pnh_container" dir="ltr" tabindex="-1"><span class="skype_pnh_mark"> begin_of_the_skype_highlighting</span> <span class="skype_pnh_highlighting_inactive_common" dir="ltr" title="Click to make a low cost call with Skype"><span class="skype_pnh_left_span" title="Skype actions">  </span><span class="skype_pnh_dropart_span" title="Skype actions"><span class="skype_pnh_dropart_flag_span" style>      </span>   </span><span class="skype_pnh_textarea_span"><span class="skype_pnh_text_span">(210) 458-6270</span></span><span class="skype_pnh_right_span">     </span></span> <span class="skype_pnh_mark">end_of_the_skype_highlighting</span></span>; E-mail subject: CTA Postdoc</span></span></p>

<p><span style="font-size:11pt;font-family:'Calibri','sans-serif'">The
 University of Texas at San Antonio is an Equal Opportunity/Affirmative 
Action Employer. As part of the application process, applicants will be 
invited to complete an online confidential and voluntary self-disclosure
 card.</span></p>


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            <h3>Requirements</h3>

            <p><strong><span style="text-decoration:underline"><span style="font-size:11pt;font-family:'Calibri','sans-serif'">Minimum Requirements</span></span></strong><span style="font-size:11pt;font-family:'Calibri','sans-serif'">:
 Ph.D. in Electrical Engineering, Computer Science, Statistics, or 
related areas. Both beginning and more senior postdoctoral candidates 
are encouraged to apply. US citizenship is not required but will be 
given priority.</span></p>
<p><strong><span style="text-decoration:underline"><span style="font-size:11pt;font-family:'Calibri','sans-serif'">Preferred Qualifications</span></span></strong><span style="font-size:11pt;font-family:'Calibri','sans-serif'">:
 Strong statistical learning skills with experience in design, analysis,
 and statistical signal processing/machine learning applied to data from
 complex experimental designs. </span></p>
<p><span style="font-size:11pt;font-family:'Calibri','sans-serif'"><br></span></p>    
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