Postdoc in machine learning for cryogenic electron microscopy - Hiring in process/Finished, not possible to apply

Job description

Cryogenic electron microscopy (cryo-EM) is a powerful imaging technique for reconstructing 3D models of biological macromolecules using transmission electron microscopy. Although this method is now able to reach near-atomic resolution for certain molecules many important challenges remain. Foremost is the issue of noise. Due to the low electron dose required to reduce specimen damage, cryo-EM images are extremely noisy, a fact that is exacerbated for small molecules that have a low signal power to begin with. As a result, only molecules above a certain size can be consistently imaged using cryo-EM and very large datasets are required to obtain a reasonable accuracy.

This project instead proposes to mitigate the high noise level using priors on the 3D molecular structures. These will be learned from data using previously obtained molecular structures as well as structures synthesized by tools like AlphaFold 2. More specifically, by training deep neural networks (DNNs) to estimate various quantities along the cryo-EM reconstruction pipeline, we can encode the natural structure of the data and reduce the effect of the noise. The resulting method will allow for more accurate reconstructions at high noise levels, reducing the expense of data collection and enabling reconstruction of small molecules.

What we offer

• A position at a leading technical university that generates knowledge and skills for a sustainable future
• Engaged and ambitious colleagues along with a creative, international and dynamic working environment
• Work in Stockholm, in close proximity to nature
• Help to relocate and be settled in Sweden and at KTH

Read more about what it is like to work at KTH

Qualifications

Requirements

• A doctoral degree or an equivalent foreign degree. This eligibility requirement must be met no later than the time the employment decision is made.
• Research expertise
• Teaching abilities
• Awareness of diversity and equal opportunity issues, with specific focus on gender equality
• Collaborative abilities
• Independence
• Knowledge and experience in Python and deep learning frameworks, such as PyTorch or TensorFlow

Preferred qualifications

• A doctoral degree or an equivalent foreign degree, obtained within the last three years prior to the application deadline
• Familiarity with aspects of the cryo-EM reconstruction problem
• Large-scale software development experience (git, package development, testing, etc.)
• Experience with techniques for optimization of algorithms

Great emphasis will be placed on personal skills.

Trade union representatives

You will find contact information to trade union representatives at KTH's webbpage.

To apply for the position

Log into KTH's recruitment system in order to apply for this position. You are the main responsible to ensure that your application is complete according to the ad.

The application must include:

• CV including relevant professional experience and knowledge. Please include links to software projects you have contributed to.
• Copy of diplomas and grades from your previous university studies. Translations into English or Swedish if the original documents have not been issued in any of these languages.
• Brief account of why you want to conduct research, your academic interests and how they relate to your previous studies and future goals. Max two pages long.

Your complete application must be received at KTH no later than the last day of application, midnight CET/CEST (Central European Time/Central European Summer Time).

About the employment

The position offered is for, at the most, two years.

A position as a postdoctoral fellow is a time-limited qualified appointment focusing mainly on research, intended as a first career step after a dissertation.

Others

Striving towards gender equality, diversity and equal conditions is both a question of quality for KTH and a given part of our values.

For information about processing of personal data in the recruitment process please read here.

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Disclaimer: In case of discrepancy between the Swedish original and the English translation of the job announcement, the Swedish version takes precedence.