Postdoc in Structural Bioinformatics
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Work assignments
In this position, you will develop a new framework for cryo-EM analysis in collaboration with another lab, using python and compiled languages for machine-learning using high-performance kernels targeting national super-computing infrastructure. You will also develop methods to integrate tools of cryo-EM analysis into this framework, add new functionality and investigate their use in the study of macromolecular biology. There are also opportunities for national and international collaboration in both software development and applied biological research.
As postdoc, you will principally carry out research. A certain amount of teaching may be part of your duties, up to a maximum of 20% of working hours.
Qualifications
To be qualified to take employment as postdoc, you must have been awarded a doctoral degree or have a foreign degree that is deemed to be equivalent to a doctoral degree. This degree must have been awarded at the latest by the point at which LiU makes its decision to employ you.
It is considered advantageous if your doctoral degree is no older than three years at application deadline for this job. If there are special reasons for having an older doctoral degree – such as taking statutory leave – then these may be taken into consideration.
We are looking for someone with experience in producing high-performance data-intensive software in a collaborative setting. You will be expected to be resourceful and innovative in developing and optimizing software performance, prioritize robustness and fidelity in its results, continually develop a quality assurance pipeline through continuous integration and testing, and scrutinize your own works as well as that of others in the team. As a postdoc you will also be expected to mentor doctorate students, and actively participate in ongoing research collaborations. You will be allotted time and resources to develop your own career goals and encouraged to cultivate your own ideas and collaborations that relate to or extend your research in the group.
Experience in structural biology or bioinformatics is not required. You will be given some time to acquaint yourself with this in practice, should you want to.
The workplace
Structural bioinformatics considers molecular structure through data, encompassing the machine-learning prediction tools like AlphaFold, the information in large-scale databases of DNA sequences and genetic evolution, as well as mining of microscopy data through image analysis. The research team led by Björn Forsberg at Linköping University focuses their efforts on electron microscopy (cryo-EM and cryo-ET) data, which reconstructs 3D models of molecular life. Specifically, next-generation tools are developed and implemented for rigorous and systematic approaches to quantify heterogeneity in 3D cryo-EM reconstructions. This subject area means we use a variety of statistical, data science & machine learning approaches. We work with collaborators all over the world to apply our methods to innovative research projects and experimental data and believe in developing generally applicable open tools and packages that can be taken up by the wider scientific community.
The team is based in the Bioinformatics division within the Department of Physics, Chemistry and Biology (IFM) at Linköping University, and has the backing of the data-driven life science (DDLS) program through Science for Life Laboratory (SciLifeLab). This s a 12-year initiative funded with a total of 3,1 billion SEK (~300M€) from the Knut and Alice Wallenberg Foundation to recruit and train the next-generation of data-driven life scientists and to create globally leading computational and data science capabilities in life science in Sweden. The program is a hub for cross-disciplinary life sciences, interdisciplinary work and engagement with industry, healthcare, and other national and international partners. The team also supports and leverages the national facility for cryo-EM microscopy at SciLifeLab Solna and Umeå, and utilizes the most modern super-computing infrastructure in Sweden (Berzelius@NSC). Close collaborations exist within the department with Nicholas Pearce for macromolecular variability analysis, and Björn Wallner and Claudio Mirabello for structure and ensemble prediction methods. International collaborations extend to e.g. the Chan-Zuckerberg Imaging Institute in San Francisco and the University of Oxford.
The employment
This employment is a temporary contract of two years with the possibility of extension up to a total maximum of three years. The employment is full-time.
Starting date by agreement.
Salary and employment benefits
The university applies individual salaries.
More information about employee benefits is available here.
Union representatives
Information about union representatives, see Help for applicants.
Application procedure
Apply for the position by clicking the “Apply” button. Your application must be received no later than 30 September, 2024.
Applications and documents received after the date above will not be considered.
Please attach your selected research publications electronically, in pdf or word format, in the application template. Research publications, e.g. monographs, which cannot be sent electronically should be sent in three sets by mail to the University Registrar at Linköping University, University Registrar, S-581 83 Linköping, Sweden. The publications must be received by Linköping University no later than the deadline for application.
Please note that printed publications will not be returned. They will be archived at Linköping University.
In the event of a discrepancy between the English translation of the job announcement and the Swedish original, the Swedish version shall take precedent.
We welcome applicants with different backgrounds, experiences and perspectives – diversity enriches our work and helps us grow. Preserving everybody’s equal value, rights and opportunities is a natural part of who we are. Read more about our work with: Equal opportunities.
We look forward to receiving your application!
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