LEAD AI Postdoctoral Research Fellow within AI enabled structure-based drug design
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At the Department of Biomedicine (https://www.uib.no/en/biomedisin) in the group of Prof. Dr. Ruth Brenk (https://www.uib.no/en/rg/brenk) there is a vacancy for a postdoctoral research fellow position within Artificial intelligence enabled structure-based drug design. The position is for a fixed term of three years and is associated with the LEAD AI project co-funded by European Union’s Horizon Europe research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 101126560 and the University of Bergen.
The position is open to an incoming candidate, see LEAD AI mobility rules
The overall research goal of the Brenk lab is to improve methods used for structure-based drug design and to apply these methods to design inhibitors for enzymes with biological relevance. A key point in our research is the interplay of theoretical and experimental methods. More information can be found on our homepage https://www.uib.no/en/rg/brenk/98283/research-brenk-lab).
The advertised position will be attached to the eHACS project (Escaping the Combinatorial Explosion: Expert-Enhanced Heuristic Navigation of Chemical Space) which is about integrating knowledge-based expert guidance, modern molecular design, and empowering AI with the goal to develop a new De novo design method. More information about eHACS can be found here: https://www.uib.no/en/rg/brenk/152446/escaping-combinatorial-explosion-expert-enhanced-heuristic-navigation-chemical-space
Currently, large research efforts are ongoing to develop AI methods suitable for De novo design, scoring functions to predict the affinities of the designed compounds, and docking methods. In fact, the field is so attractive that new methods are almost published weekly. However, the validation of the AI methods is often very limited. Mostly, no activity data for AI-generated or -scored compounds is presented. If such data is included, it is typically limited to protein kinases which are considered to be easy targets due to wealth of published data.
The lack of validation using real-life drug discovery examples is seriously hampering the uptake of the methods in the field. Therefore, in this project we will work on validation of AI-based De novo design, scoring and docking methods using both retrospective and prospective predictions.
For the latter, we will make use of well studied systems in the Brenk group such the enzymes FabF and NMT for which we can routinely obtain binding data and high resolution crystal structures (Espeland LO, Georgiou C, Klein R, Bhukya H, Haug BE, Underhaug J, Mainkar PS, Brenk R.
An Experimental Toolbox for Structure-Based Hit Discovery for P. aeruginosa FabF, a Promising Target for Antibiotics. ChemMedChem. 2021., Kersten C, Fleischer E, Kehrein J, Borek C, Jaenicke E, Sotriffer C, Brenk R. How To Design Selective Ligands for Highly Conserved Binding Sites: A Case Study Using N-Myristoyltransferases as a Model System. J Med Chem. 2020). The lessons learned from the validation shall also be used to develop improved methods.
About the LEAD AI fellowship programme
LEAD AI is the University of Bergen's career and mobility fellowship program for training 19 postdoctoral fellows in artificial intelligence.
The program has received funding from the European Union’s Horizon Europe research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 101126560.
The LEAD AI program offers high-quality inter- and transdisciplinary research and training opportunities in the area of artificial intelligence supported by a dedicated supervision and mentoring, encouraging inter-sectoral exposure, in particular:
For more information see the LEAD AI webpage or send an email to leadai@uib.no
Personal and relational qualities will be emphasized. Research experience, ambitions and potential will also be considered during candidate evaluation.
Special requirements for the position
The University of Bergen is subjected to the regulation for export control system. The regulation will be applied in the processing of the applications.
The position of postdoctoral research fellow is a fixed-term appointment with the primary objective of qualifying the appointee for work in top academic positions. You cannot be employed as a postdoctoral fellow for more than one fixed term period at the same institution.
The position may be extended by up to one year (maximum 4 years in total) if the appointee is granted externally financed research stays abroad.
Individuals may not be hired for more than one fixed-term period as a postdoctoral research fellow at the same institution.
For all LEAD AI fellows, a Personal Career Development Plan (PCDP) will be developed jointly by the fellow, supervisor, and co-supervisor by the end of Month 3 of the fellowship, including a plan for the individual research budget, and information on additional funding where applicable.
It is a requirement that the project is completed in the course of the period of employment.
*) Subject to membership in the Norwegian National Insurance Scheme.
**) Right to paid parental leave requires 6 months paid work before first day of leave. See full requirements.
How to apply:
Before starting the online application process, please familiarise yourself carefully with our application requirements in the Guide for Applicants and Application templates It is essential that all required attachments (see next section) are uploaded via our electronic recruiting system JobbNorge. Before uploading any documents in the portal (to minimise repetition of information):
The application and appendices with certified translations into English or a Scandinavian language must be uploaded at Jobbnorge
Evaluation
We anticipate the whole evaluation procedure to take approximately 4 months from application deadline. Eligible applicants will be evaluated by three internationally renowned experts and assessed against criteria addressing excellence, impact, implementation, quality of the researcher and training, and knowledge transfer. Details are stated in the Guide for applicants.
For further details about the position, please contact:
Diversity is a strength that enables us to solve our tasks even better. UiB therefore needs qualified employees regardless of gender, ethnicity, religion, worldview, disability, sexual orientation, gender identity, gender expression, and age.
The University of Bergen applies the principle of public access to information when recruiting staff for academic positions.
Information about applicants may be made public even if the applicant has asked not to be named on the list of persons who have applied. The applicant must be notified if the request to be omitted is not met.
We encourage applicants with disabilities, immigrant backgrounds, or gaps in their CV to apply. By indicating such circumstances in your application, you may receive favourable consideration. We ensure that at least one qualified applicant from each of these groups is invited for an interview as part of our commitment to inclusivity and equal opportunity.
Further information about our employment process can be found here.
The University of Bergen (UiB) is an internationally recognised research university with more than 14,000 students and close to 3,500 employees at six faculties. The university is located in the heart of Bergen.
