[Neuroimaging] [JOB] Post Doc Machine Learning for neuroscience time series

Alexandre Gramfort alexandre.gramfort at telecom-paristech.fr
Thu Jun 30 12:50:32 EDT 2016

*Post-Doc/Research position in:*

*Supervised learning on neuroscience time-series with application to

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/> and Bioserenity
<http://www.bioserenity.com/> as well as the ICM Institute
<http://icm-institute.org/fr/> at the Salpétrière 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

*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

- Alexandre Gramfort  (alexandre.gramfort at telecom-paristech.fr)

- Slim Essid (slim.essid at telecom-paristech.fr)
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