In this project, the successful candidate will contribute to the development of a computational framework based on active learning to guide the search for materials that catalyze the conversion of CO2 to methanol at low temperatures. In recent work, Mie Andersen’s group has developed machine learning models for the prediction of catalytically relevant parameters such as adsorption energies for a wide range of molecules and active site motifs at metal and oxide catalysts. The predictive models can, once trained, replace expensive first-principles calculations and provide direct input to thermodynamic or microkinetic models of catalytic activity and selectivity. The postdoc will be introduced to these methods and is expected to further develop them into an active learning framework that, among others, will include the use of uncertainty estimates to carry out global sensitivity analysis and uncertainty quantification of microkinetic models.
You can learn more about Mie Andersen’s research activities here.
Machine Learning for Chemical Physics
In this project, the successful candidate(s) will identify and develop machine learning techniques that may speed up first principles calculations of equilibrium structures and reaction pathways in chemical physics. In recent years, Bjørk Hammer’s group has developed a number of techniques for global optimization (see: https://gofee.au.dk). One method, GOFEE, does its structural search in a model potential using Gaussian Process Regression and is guided by Bayesian statistics to perform occasional sanity checks with full density functional theory (DFT) calculations. Another method, ASLA (see: https://asla.au.dk), does self-training of image recognition for neural network agents that interact with a DFT program. The postdoc(s) will be introduced to these methods and be expected to develop their own improvements and to apply these in the search for the reactive state of matter ranging from interstellar dust clouds to industrial heterogeneous catalysts.
You can learn more about Bjørk Hammer’s group here.
Your Profile
Applicants must hold a PhD degree in physics, chemistry, nanoscience, computer science or equivalent. Previous experience with machine learning methods and/or first principles energy calculations in physical chemistry is required. Experience with programming in python is highly desired.
Place of work and area of Employment
The place of work is Ny Munkegade 120, 8000 Aarhus C and the area of employment is Aarhus University with related departments.
Contact information
Further information may be obtained by e-mailing Mie Andersen (mie@aias.au.dk) or Bjørk Hammer (hammer@phys.au.dk). Please use as e-mail subject: "postdoctoral position 21".
Application procedure
Shortlisting is used. This means that after the deadline for applications – and with the assistance from the assessment committee chairman, and the appointment committee if necessary, – the head of department selects the candidates to be evaluated. All applicants will be notified whether or not their applications have been sent to an expert assessment committee for evaluation. The selected applicants will be informed about the composition of the committee, and each applicant is given the opportunity to comment on the part of the assessment that concerns him/her self. Once the recruitment process is completed a final letter of rejection is sent to the deselected applicants.Letter of reference
If you want a referee to upload a letter of reference on your behalf, please state the referee’s contact information when you submit your application. We strongly recommend that you make an agreement with the person in question before you enter the referee’s contact information, and that you ensure that the referee has enough time to write the letter of reference before the application deadline. Unfortunately, it is not possible to ensure that letters of reference received after the application deadline will be taken into consideration.Formalities and salary range
Natural Sciences refers to the Ministerial Order on the Appointment of Academic Staff at Danish Universities under the Danish Ministry of Science, Technology and Innovation.The application must be in English and include a curriculum vitae, degree certificate, a complete list of publications, a statement of future research plans and information about research activities, teaching portfolio and verified information on previous teaching experience (if any). Guidelines for applicants can be found here.
Appointment shall be in accordance with the collective labour agreement between the Danish Ministry of Finance and the Danish Confederation of Professional Associations. Further information on qualification requirements and job content may be found in the Memorandum on Job Structure for Academic Staff at Danish Universities.
Salary depends on seniority as agreed between the Danish Ministry of Finance and the Confederation of Professional Associations.
All interested candidates are encouraged to apply, regardless of their personal background. Research activities will be evaluated in relation to actual research time. Thus, we encourage applicants to specify periods of leave without research activities, in order to be able to subtract these periods from the span of the scientific career during the evaluation of scientific productivity.
Aarhus University offers a broad variety of services for international researchers and accompanying families, including relocation service and career counselling to expat partners. Read more here. Please find more information about entering and working in Denmark here.
Aarhus University also offers a Junior Researcher Development Programme targeted at career development for postdocs at AU. You can read more about it here.
The application must be submitted via Aarhus University’s recruitment system, which can be accessed under the job advertisement on Aarhus University's website.
Questions about the position?
Mie AndersenAssociate professor, AIAS-COFUND Fellow, Aarhus Institute of Advanced Studiesmie@aias.au.dk
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