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CCPEM  September 2018

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Subject:

FW: Job Opening - Computational Scientist - STFC / CCP-EM

From:

Martyn Winn - UKRI STFC <[log in to unmask]>

Reply-To:

Martyn Winn - UKRI STFC <[log in to unmask]>

Date:

Fri, 7 Sep 2018 08:09:59 +0000

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (48 lines)

Reminder that the closing date for applications is on Sunday. Please contact me or Tom if any queries.

Cheers
Martyn

-----Original Message-----
From: Collaborative Computational Project in Electron cryo-Microscopy [mailto:[log in to unmask]] On Behalf Of Tom Burnley - UKRI STFC
Sent: 17 August 2018 12:32
To: [log in to unmask]
Subject: [ccpem] Job Opening - Computational Scientist - STFC / CCP-EM

Dear all,


We are seeking a computational scientist, focusing on the application of machine learning to cryoEM.


This permintant position will be embedded in a new team in the Scientific Computing Department of STFC and based at the Rutherford Appleton Laboratory in Oxfordshire, UK, home to the Diamond Light Source, the ISIS Neutron and Muon Source and the Central Laser Facility.  The team will consistent of several developers who will apply machine learning to scientific data across multiple domains.  The EM specific role will be guided by CCP-EM and will work closely with the national EM facility (eBIC / DLS).  For informal enquiries please contact myself: Tom Burnley ([log in to unmask]) or Martyn Winn ([log in to unmask]).


All the best,


Tom


Job desciption:


This opening is a great opportunity that combines two of the most exciting subjects in life science: Machine Learning (ML) and cryo-electron microscopy (cryoEM).  Following recent progress in electron cryo-microscopy (cryo-EM), as recognised by the 2017 Nobel Prize in Chemistry, it is now possible to determine structures of biological macromolecules in atomic detail from images of molecules in frozen solution. This revolution has led to major investments in cryoEM, including at the Electron Bio-Imaging Centre (eBIC) at Diamond on the Harwell campus. Such facilities are generating large amounts of data daily, and the bottleneck has moved to data processing. At the same time, AI and machine learning techniques are coming of age, and promising fast and reliable data-driven analysis of data.

STFC have created the Scientific Machine Learning (SciML) group to focus efforts on applying the latest AI and machine learning techniques to large experimental datasets. This role, one of several being created in the SciML group, will develop and apply advanced data analytical techniques to cryoEM datasets, in close collaboration with eBIC.

Further details and application:

https://www.nature.com/naturejobs/science/jobs/650553-computational-scientist

http://www.topcareer.jobs/Vacancy/irc245954_8504.aspx

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