[Eeglablist] MNE Postdoc opportunities @ Harvard Medical School & Massachusetts General Hospital

[Sheraz Khan]

On behalf of Dr. Matti Hämäläinen (msh at nmr.mgh.harvard.edu)

[Apologies for cross posting]

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The academic MEG/EEG software packages are becoming more and more important
to the field as new new sensor technologies are being applied and new
commercial MEG systems are becoming available. We welcome new young
scientists to become part of this endeavor!

With best regards,

Matti

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There are two postdoctoral positions available in the MNE projects at:

Athinoula A. Martinos Center for Biomedical

Massachusetts General Hospital and Harvard Medical School



*Position 1: Postdoctoral Research Fellow (MNE-Python)*



*Overview:* Applications are invited for a Postdoctoral Research Fellow
available at the Massachusetts General Hospital Athinoula A. Martinos
Center for Biomedical Imaging (www.nmr.mgh.harvard.edu). The postdoctoral
fellow will also be affiliated with Harvard Medical School. We are using
electrophysiological recordings in combination with anatomical and
functional MRI to understand the functions of the healthy brain and how
they are altered in neurological and psychiatric diseases. We capitalize on
the insights from our basic and clinical research to develop new methods
and tools to be applied in clinical practice.

*Responsibilities:* The postdoc will work in an NIH-funded project whose
overall aim is to provide well-documented and tested novel analysis
software to promote both basic neuroscience and clinical research
applications using MEG and EEG. In particular, during the past eight years
we have developed, with NIH support, the MNE-Python software http://www.-
martinos.org/mne/, which covers multiple methods of data preprocessing,
source localization, statistical analysis, and estimation of functional
connectivity between distributed brain regions. In this new project we will
extend our software to meet the needs of a growing user base and reflect
recent developments in the MEG/EEG field.

Requirements: Candidates should have a Doctoral degree in biomedical
engineering, computer science or related fields. Strong Python programming
skills are required and background and experience in acquiring and
analyzing MEG/EEG data are highly desirable. Candidates need to be able to
work in a modern software development environment and be familiar with
controlled software development processes. A successful candidate is able
to work in a dynamic, international, and collaborative work environment.
He/She will gain experience in recording and analyzing electrophysiological
data, working with state-of-the-art brain mapping technologies as well as
engaging in top-level research. Familiarity with MEG/EEG and MRI analysis
software packages, in particular MNE-Python and FreeSurfer is highly
beneficial.

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*Position 2: Postdoctoral Research Fellow (MNE-Scan)*



*Overview:* Applications are invited for a postdoctoral position available
at the Massachusetts General Hospital Athinoula A. Martinos Center for
Biomedical Imaging (http://www.nmr.mgh.harvard.edu). The postdoctoral
fellow will also be affiliated with Harvard Medical School. Our MEG/EEG
laboratory is using electrophysiological recordings in combination with
anatomical and functional MRI to understand the functions of the healthy
brain and how they are altered in neurological and psychiatric diseases.
Our ultimate goal is to capitalize on the insights from our basic and
clinical research to develop new methods and tools to be applied in
clinical practice.

*Responsibilities:* The Postdoctoral fellow will work in an NIH-funded
project which aim is to develop a device-independent and real-time capable
software (MNE Scan). MNE Scan is based on the MNE-CPP framework (
www.mne-cpp.org) and will significantly increase the ease and efficiency of
acquiring, monitoring, analyzing, and integrating electroencephalography
(EEG), magnetoencephalography (MEG), electrocorticography (ECoG), and
stereotactic EEG (SEEG) data. The MNE Scan software will format incoming
electrographic data from any recording device for EEG, ECoG, SEEG, and MEG,
store the data, carry out preprocessing for noise reduction and signal
conditioning, display the incoming data in real time, and carry out the
data analysis at the level of active tissues instead of the sensor level.
Its realtime capability will provide immediate feedback to clinicians,
enabling them to use this information for improving diagnosis and surgical
management of epilepsy. Furthermore, MNE Scan will be used during
neurofeedback experiments, including brain state dependent stimulation. The
Postdoctoral fellow will work with other members of the investigative team
to identify and develop new scientific use cases and research questions for
the MNE Scan software described above. He will also work together with
other investigators in the team to enhance source estimation and
connectivity estimation methods to work efficiently in the real-time
environment.

*Requirements:* The successful candidate must hold a Ph.D. degree in
biomedical engineering, computer science, or related fields. A successful
candidate will benefit from working in a dynamic, international, and
collaborative supportive work environment. He/She will gain experience in
recording and analyzing electrophysiological data, working with
state-of-the-art brain mapping technologies as well as engaging in
top-level research. Previous experience in acquiring and analyzing MEG/EEG
data is a strength. Strong C++ programming skills and experience in the Qt
toolkit are highly desirable. It is preferred that the candidate has
familiarity with MEG/EEG and MRI analysis software packages, in particular
MNE and FreeSurfer.

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Welcome to the MNE Family!

Massachusetts General Hospital is an equal opportunity employer. Full-time
employees receive full benefits, competitive salaries, and excellent
resources for career development.

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*Contact: Matti Hämäläinen *

*msh at nmr.mgh.harvard.edu <msh at nmr.mgh.harvard.edu>*

*Athinoula A. Martinos Center for Biomedical*

*Massachusetts General Hospital and Harvard Medical School*

*Boston, MA, USA*

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