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<p class="MsoNormal"><b><span lang="EN-US" style="font-size:12.0pt;line-height:105%">Postdoctoral fellow in AI, and real time signal processing for Brain Computer Interface clinical application</span></b><span lang="EN-US" style="font-size:12.0pt;line-height:105%"><o:p></o:p></span></p>
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<span lang="EN-US">The post-doctoral fellowship will be carried out within the frame of the multidisciplinary project “Brain Computer Interface” (BCI) at CEA-LETI-CLINATEC, Grenoble, France, in collaboration with the Data Intelligence Service from
<span style="color:black">CEA</span>-LIST <span style="color:black">(Paris-Saclay), France.
</span>The goal of the BCI project is the proof of concept that it is possible to control complex effectors thanks to brain activity decoding. Motor BCI raises the hope that limb mobility may be restored for severely motor-impaired patients to regain autonomy,
providing them with control over orthosis or prostheses.<o:p></o:p></span></p>
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<span lang="EN-US">The project is based on the recording of neuronal activity at the level of cerebral motor cortex (ElectroCorticoGrams, ECoG) with innovative wireless WIMAGINE® implant dedicated to the long term clinical use. A clinical research protocol
« BCI and Tetraplegia » at CLINATEC® includes several tetraplegic subjects and is in progress. In this general frame, the project of partners CEA-LETI-CLINATEC and CEA-LIST “Enabling out-of-laboratory neuroprosthetics for severely motor-impaired patients using
artificial intelligence”, is supported by Programme Transversal de Compétences « Simulation Numérique » of CEA.
<o:p></o:p></span></p>
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<span lang="EN-US">The particular objective of the project is moving toward neuroprosthetics out-of-the-lab applications. This goal entails solving major challenges: increase number of Degrees of Freedom (DoF), improving accuracy and robustness of neural signal
decoding to ensure usability of neuroprosthetics for real life tasks from one side, and providing a BCI system for constrained execution environments offered by portable devices from another side. One direction to improve decoding performances is to use recent
advances in artificial neural networks. To ensure that the decoding algorithm is compatible with constrained environments, including the restrained CPU and memory costs such that real-time signal processing and decoding remains feasible, various strategies
could be investigated.<o:p></o:p></span></p>
<p class="MsoNormal" style="text-align:justify"><span lang="EN-US">Missions of post-doctoral fellow will include:<o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:18.0pt;margin-bottom:.0001pt;text-align:justify;text-indent:-18.0pt;line-height:normal;mso-list:l0 level1 lfo2">
<![if !supportLists]><span lang="EN-US" style="font-family:"Times New Roman",serif"><span style="mso-list:Ignore"><span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span lang="EN-US">The experiment design and set up for real life tasks of using of neuroprosthesis by tetraplegic subject;
<o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:18.0pt;margin-bottom:.0001pt;text-align:justify;text-indent:-18.0pt;line-height:normal;mso-list:l0 level1 lfo2">
<![if !supportLists]><span lang="EN-US" style="font-family:"Times New Roman",serif"><span style="mso-list:Ignore"><span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span lang="EN-US">Design of innovative ANN decoding algorithms, the test and the comparison to conventional methods;
<o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:18.0pt;margin-bottom:.0001pt;text-align:justify;text-indent:-18.0pt;line-height:normal;mso-list:l0 level1 lfo2">
<![if !supportLists]><span lang="EN-US" style="font-family:"Times New Roman",serif"><span style="mso-list:Ignore"><span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span lang="EN-US">Optimization and implementation for real time application, integration to the Clinatec BCI platform;<o:p></o:p></span></p>
<p class="MsoHeader" style="margin-top:12.0pt;text-align:justify"><span lang="EN-US" style="font-size:11.0pt;font-family:"Calibri",sans-serif">A unique clinical trial ECoG database supports the project.<o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:12.0pt;margin-right:0cm;margin-bottom:0cm;margin-left:0cm;margin-bottom:.0001pt;text-align:justify">
<span lang="EN-US">Initial contract duration is 12 months with possible prolongation (24 months at max), initial 12 months in
<span style="color:black">CEA / DRT / LETI / CLINATEC, </span>Grenoble and the next ones in
<span style="color:black">CEA / DRT / List / DM2I / SID / LI3A, Saclay</span>.<o:p></o:p></span></p>
<p class="MsoNormal" style="margin-top:12.0pt;text-align:justify"><b><span lang="EN-US">Profile of candidate:
</span></b><span lang="EN-US">PhD or equivalent with strong knowledge in Machine learning, Deep learning, real time Signal processing (high dimensional data flow), with strong skills in
<span style="color:black">Python, matlab.</span><b><o:p></o:p></b></span></p>
<p class="MsoNormal"><b><span lang="EN-US">Application:</span></b><span lang="EN-US" style="color:black"> The candidates should send a CV,</span><span lang="EN-US"> the names of 2 referees,
<span style="color:black">and a cover letter to Dr. Tetiana AKSENOVA, <span class="MsoHyperlink">
<span style="color:black;text-decoration:none"><a href="mailto:tetiana.aksenova@cea.fr"><span style="color:black;text-decoration:none">tetiana.aksenova@cea.fr</span></a>, Dr.
</span></span>Cedric GOUY-PAILLER, <a href="mailto:cedric.gouy-pailler@cea.fr">cedric.gouy-pailler@cea.fr</a>, and Dr. Pierre BLANCHART,
<a href="mailto:pierre.blanchart@cea.fr">pierre.blanchart@cea.fr</a>. The selected candidates will be interviewed for an expected start in March 2019.<o:p></o:p></span></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
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