PhD Scholarships – Graduate School of Natural Sciences, Aarhus University

    PhD Scholarships - Graduate School of Natural Sciences, Aarhus University

    Few PhD Scholarships in Geoscience, Nanoscience, Physics and Astronomy at Graduate School of Natural Sciences, Aarhus University, Denmark

    Deadline to Apply

    May 01, 2021 (23: 59 GMT +1)


    PositionPhD Position
    No. of Position(s)One or more
    Research AreaGeoscience
    Physics and Astronomy
    ScholarshipAccording to Standard Norms
    WorkplaceGraduate School of Natural Sciences
    Aarhus University
    Katrinebjergvej 89F, building 5132
    8200 Aarhus N
    Contract PeriodNot specified


    Graduate degree in the relevant field


    According to the relevant research programme

    How to Apply?

    To access the application form, click the relevant field below:

    Fill in the following information:

    • Personal information
    • Academic background
    • Admission
    • Financing (if any)
    • Study: In the dropdown menu you must choose the above-mentioned project you interested for example “Surface nuclear magnetic resonance for high-resolution mapping of groundwater resources
    • Source (how you found out about the call) Please mention that “nViews Career”

    Next to some of the information fields you will find a number. Click on the number to get further directions on how to fill in the information field/what information is needed.

    Documents Required

    • One reference (template for references)
    • Curriculum vitae,
    • Motivation (max. 1 page)
    • Transcripts, grade point averages (weighted and unweighted), and diploma(s) for both Bachelor’s and Master’s degree. If the original documents are not in English or one of the Scandinavian languages (i.e. Norwegian, Swedish or Danish) then copies of the original documents as well as a certified English translation must be attached.
    • Use this option – > Project description (½-4 pages). For technical reasons, you must upload a project description. When – as here – you apply for a specific project, please simply copy the project description above, and upload it as a PDF in the application. If you wish to, you can indicate an URL where further information can be found. Please note that we reserve the right to remove scientific papers, large reports, theses and the like.
    • Project description (½-4 pages). This document should describe your ideas and research plans for this specific project. If you wish to, you can indicate an URL where further information can be found. Please note that we reserve the right to remove scientific papers, large reports, theses and the like.
    • Documentation of language skills if required.

    After submission of the application, you will receive a confirmation e-mail with an application ID, you should use for reference if needed.

    About Graduate School of Natural Sciences

    The Basic principles below have been decided at AU level following discussions among the Heads of the graduate schools at AU and involvement from for instance the PhD committees. The aim has been to create basic principles at AU level for the PhD education to ensure as it says ‘the PhD students’ academic development by addressing the appropriate level of independence at different stages of the PhD education’.

    Surface nuclear magnetic resonance for high-resolution mapping of groundwater resources

    Access to safe and sustainable water resources is key to sustaining human life. In many cases, groundwater represents an attractive solution to meeting daily water needs, as it is often at lower risk of contamination compared to surface water sources and may be accessible in regions where surface water sources are unavailable or not accessible across the entire year. Yet, locating and sustainably managing these resources remains a difficult task; particularly in regions where one has little to no-preexisting knowledge of local groundwater systems.

    One geophysical tool that shows great promise to aid in location and management of groundwater resources is surface nuclear magnetic resonance (NMR). Surface NMR is the only non-invasive geophysical tool providing direct sensitivity to water at depth, providing unambiguous insights into water’s presence. However, surface NMR commonly suffers from low signal qualities – a limitation that greatly restricts mapping speeds/coverage and the types of environments where the method can be employed.

    Carbonation Curing of Cementitious Systems (C3S): solid-state NMR studies of new cement binders

    Concrete is the world’s most durable, reliable and economical construction material with an annual consumption in volume by society only surpassed by water. Portland cement represents the “glue” in conventional concrete, and its word-wide production is responsible for 7 – 8 % of the man-made CO2 emissions. The CO2 emission originates from decarbonation of limestone (CaCO3) and combustion of fuels for heating the cement kilns. Concrete is produced by hydration of blends of Portland cement, sand and gravel where the hydrate phases are formed by dissolution – precipitation reactions.

    The aim of the project is to explore the chemistry of new binders consisting of low-CaO calcium silicates, where the hydraulic cement reactions are replaced by carbonation hardening. In this process, the calcium silicates react with CO2 gas under sealed conditions at slightly elevated temperatures. The process can lead to new types of hardened materials with similar strengths as Portland cement based concrete but produced with a reduced CO2 emission of approx. 70 %. These new types of binders are suitable for prefabricated concrete elements, which constitute roughly 20 % of the concrete used today.

    Understanding the reactivity of catalytic materials with machine learning

    Materials with a catalytic function may be found in such diverse places as chemical reactors or as dust grains in molecular clouds in the interstellar space. Despite their crucial role in society and the Universe for accelerating chemical reactions, the reliable description of catalytic properties and the prediction of what materials may be even better catalysts than those we know already is still challenging. As catalysts are typically rather complex and may consist of different types of (nanostructured) materials, experiments alone are often insufficient to understand the factors controlling their reactivity or to identify the “active sites” responsible for the actual catalytic effect. Predictive-quality theory and computer simulations may provide crucial input, e.g. in the form of adsorption energies of key atoms or molecules at different types of active site motifs of the material. While it is possible to use quantum mechanical calculations (density functional theory, DFT) to study simple reactions and simple model catalysts such as high-index facets of metals or oxides, the computational demands of such an approach can quickly become prohibitively large for realistic materials, i.e. interstellar dust grains in the Universe or the catalytic materials that are actually present inside chemical reactors.

    In this project, the successful PhD candidate will develop and apply machine learning (ML) techniques to help tackle these challenges. ML can both help us speed up the prediction of key catalytic parameters such as adsorption energies compared to DFT and allow us to gain physical insights into the key factors controlling catalytic reactivity through interpretation of the patterns in the data that the ML model has learned [1-3]. The PhD candidate will benefit from an inspiring international environment and collaboration with both theoretical and experimental partners within the Center for Interstellar Catalysis at the Department of Physics and Astronomy.


    Mie Andersen

    Department of Chemistry
    Langelandsgade 140
    DK-8000C Aarhus, Denmark
    Phone +45 2899 2029

    Denys Grombacher, Assistant Professor, Department of Geoscience
    Email :

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    Post expires at 8:59am on Sunday May 2nd, 2021 (GMT+9)