Post-Doctoral Research Associate in Cryo-EM data analysis, Open to flexible working
(3 year Fixed Term Contract)
We invite applications for a Post-Doctoral Research Fellow to work between the UK Structural & Biophysical Sciences Group within GlaxoSmithKline (GSK) R&D and the Cambridge Image Analysis Group at the Department of Applied Mathematics and Theoretical Physics at the University of Cambridge. The candidate will be part of a multidisciplinary project on inverse problems related to electron cryo-microscopy (cryo-EM) aiming to accelerate data analysis and apply novel methods to real life therapeutic examples and co-supervised by Dr. Chun-wa Chung (GSK) and Professor Carola-Bibiane Schönlieb (DAMPT, U. Cambridge.)
GlaxoSmithKline (GSK) is a world leading research-based pharmaceutical company that combines a talented workforce and technical resources to create a platform for the delivery of strong growth in a rapidly changing healthcare market. Our mission is to improve the quality of human life by enabling people to do more, feel better and live longer.
The UK Structural & Biophysical Sciences (SBS) group is a multi-disciplinary department within GSK R&D that provides molecular insights into drug discovery for both small molecule and biopharmaceutical programs. We are a vibrant team with broad expertise that spans structural biology and biophysical techniques as well as data sciences.
The Cambridge Image Analysis (CIA) group specialises in the mathematics of inverse problems in imaging, digital image and video processing using partial differential equations, variational methods and machine learning. The group conducts research ranging from the modelling and analysis of such methods to their computational realisation and application. The group also has several active research collaborations on inverse problems related to cryo-EM with world leaders in Cambridge and elsewhere.
The project will focus particularly on the design and implementation of mathematical and machine learning methods for cryo-EM/ET image and atomistic model reconstruction to deliver scientific insights at pace. This is an exceptional opportunity to conduct ambitious research while collaborating across interdisciplinary teams in industry and academia.
You will be a highly driven individual with an excellent track record of success and deep expertise in mathematical method development. A desire and ability to bridge between disciplines and programs will be an expectation.
You will become an integral member of cryo-EM project teams in both the SBS and CIA groups, efficiently developing and conducting both individual and collaborative research. You will be expected to plan and manage your own research objectives, proposals, and projects, with guidance as required.
You must be a collaborative team player able to work successfully within diverse teams. You will be good at multi-tasking and capable of good computational study record-keeping, with excellent written and oral communication skills. An ability to work towards agreed timelines and effectively organise work will be considered a key asset of the successful applicant.
Key Responsibilities include, but are not limited to:
- Development of robust and automated pipeline processes for single particle cryo-EM applications.
- Working with scientist to test these approaches on systems of biological interest.
- Advancing data processing approaches for heterogeneous samples.
- Exploring the opportunities and benefits of new data collection and/or hardware developments on accelerating Single-particle and cryo-ET applications
- To take initiative in the careful planning of research.
- To actively and enthusiastically participate in the research programme of the team.
- To identify, develop and implement suitable experimental protocols, utilising expertise from key collaborators, where appropriate.
- To maintain data confidentiality at all times.
- To maintain highly organised and accurate research records.
- To write reports for submission to research sponsors.
- To publish in high-quality journals.
- To participate in team research meetings and internal seminars.
- To actively pursue opportunities to broaden appropriate skills portfolio.
- Any other duties as may be deemed reasonable by supervisors.
Closing Date for Applications: 17th May 2021 (COB)
Please take a copy of the Job Description, as this will not be available post closure of the advert.
When applying for this role, please use the 'cover letter' of the online application or your CV to describe how you meet the competencies for this role, as outlined in the job requirements above. The information that you have provided in your cover letter and CV will be used to assess your application.
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We are looking for professionals with these required skills to achieve our goals:
- A PhD degree in mathematics, statistics, or physical sciences with a strong mathematical focus.
- Experience, expertise and demonstrated track record in one or more of the following:
- Inverse problems;
- Mathematical imaging;
- Machine learning;
- cryoEM image analysis;
- Computational analysis;
- Data science;
- Demonstrated ability to develop innovative mathematical methods and realize them computationally.
- An interest in working at the interface between industry and academia.
- Track record of innovation to solve challenging problems.
- Strong interpersonal, communication and team working skills.
- A meticulous approach and attention to detail.
- Flexible attitude towards work.
- Discipline and regard for confidentiality and security at all times.
If you have the following characteristics, it would be a plus:
- Experience in parallel computing and python/C programming.
- Demonstrated ability to communicate with and develop solutions with scientists.
- Experience integrating new data analyses into workflows to deliver insights at pace.
- An understanding of key unsolved problems in structural biology.
Prospective candidates with some of the desired attributes are encouraged to apply.
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- Committed to delivering high quality results, overcoming challenges, focusing on what matters, execution.
- Continuously looking for opportunities to learn, build skills and share learning.
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- Budgeting and cost-consciousness.
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