# \[JOB\] Post Doc Machine Learning for neuroscience	time series

**URL:** <https://mne.discourse.group/t/job-post-doc-machine-learning-for-neuroscience-time-series/1038>\
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**Created:** [June 30, 2016, 4:50pm UTC](https://mne.discourse.group/t/job-post-doc-machine-learning-for-neuroscience-time-series/1038 "2016-06-30T16:50:32Z")\
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**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\
**Post date:** [June 30, 2016, 4:50pm UTC](https://mne.discourse.group/t/job-post-doc-machine-learning-for-neuroscience-time-series/1038/1 "2016-06-30T16:50:32Z")

</div>

\*Post-Doc/Research position in:\*

\*Supervised learning on neuroscience time-series with application to  
epilepsy\*

Place: TELECOM ParisTech, 75013 Paris, France

Duration: 1 year (extension possible)

Start: Any date from September 1st, 2016

Salary: according to background and experience

\*Keywords:\* machine learning, time-series, optimization, conditional random  
fields (CRF), representation learning, electroencephalography (EEG)

\*Position description\*

The objective of the project is to develop machine learning tools that can  
facilitate the review, the visualization, the processing and the annotation  
of clinical intracranial EEG data in the context of epilepsy. Epilepsy is a  
pathology that leads to prototypical patterns in the recorded time series  
(spikes, high frequency oscillations, seizures). The objective of the  
project is to build algorithms that automatically pinpoint such events in  
raw intracranial EEG data. The approach envisioned is based on  
state-of-the-art machine learning techniques (representation learning,  
conditional random fields).

The position is funded by a joint grant between Alexandre Gramfort and Slim  
Essid at the Signal and Image Processing department at Telecom ParisTech,  
the companies Dataiku \<[http://www.dataiku.com/](http://www.dataiku.com/)\> and Bioserenity  
\<[http://www.bioserenity.com/](http://www.bioserenity.com/)\> as well as the ICM Institute  
\<[http://icm-institute.org/fr/](http://icm-institute.org/fr/)\> at the Salp?tri?re hospital.

\*Work Environment\*

TELECOM ParisTech is the leading graduate School of Institut TELECOM with  
more than 160 research professors and over 250 Engineering degrees, 50 PhD  
and 150 specialized masters (post graduates) awarded per year. The signal  
and image processing (TSI) department conducts leading research in the  
field of statistics, machine learning and signal processing with regular  
publications in leading conferences (NIPS, ICML, ICCASP, etc.) and journals  
(JMLR, IEEE Trans. Med. Imaging, IEEE Trans. Signal Processing, etc.).

The candidate will be integrated to a team formed by 6 PIs, more than 10  
PhD students, 4 engineers and 3 post-docs, among which are 6 persons  
dedicated to the statistical analysis of electrophysiological signals. The  
local expertise is unique with both significant experience in signal  
processing, machine learning, statistics and in applied neuroscience data  
munging.

\*Candidate Profile\*

As minimum requirements, the candidate will have:

a PhD in computer science, statistics / machine learning, signal processing

strong programming skills (Experience with Python is a definite plus)

strong communication skills in English.

The ideal candidate would also have:

- Prior experience with EEG data analysis and/or electrophysiology signals

- Ability to work in a multi-partner collaborative environment.

- Basic knowledge of French (not required).

\*Contacts \*Interested applicants can contact Alexandre Gramfort or Slim  
Essid for more information or directly email a candidacy letter including a  
Curriculum Vitae, a list of publications and a statement of research  
interests.

- Alexandre Gramfort (alexandre.gramfort at [telecom-paristech.fr](http://telecom-paristech.fr))

- Slim Essid (slim.essid at [telecom-paristech.fr](http://telecom-paristech.fr))  
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