Postdoc position in Applied Machine Learning, Denmark

    Postdoc position in Applied Machine Learning, Denmark

    Postdoc position in Applied Machine Learning for Accelerated Battery Innovation, in the Section for Atomic Scale Materials Modelling at DTU Energy, Technical University of Denmark


    Deadline to Apply

    June 27, 2021 (23: 59 GMT +1)


    Overview

    PositionPostdoc position
    No. of Position(s)1
    Research Area– Applied Machine Learning
    – Materials or physical sciences
    SalaryAccording to standard norms
    WorkplaceSection for Atomic Scale Materials Modelling
    DTU Energy
    Technical University of Denmark
    Denmark
    Contract Period2 Years
    Starting dateSep 01, 2021

    Qualifications

    Candidates should hold a PhD or equivalent degree in computer science, physics, chemistry, or materials science. The candidates must have a strong background in applied machine learning, preferably coupled with experience in materials or physical sciences, and are expected to have performed original scientific research within the fields listed above. Moreover, the successful candidate:

    • is innovative and learns new concepts and methods on the fly
    • has good communication skills in English, both written and spoken
    • gets excited by challenging complex projects
    • can work independently within a cross-disciplinary team and take responsibility for the progress and quality of projects.

    Responsibilities/ Job Description

    Project descriptionsThe successful candidates will break new ground in developing and applying machine learning (ML) methods that leverage multi-sourced experimental and computational data towards building ML models that capture atomic-scle phenomenon at the solid-liquid interfaces present inside the lithium-ion battery and guides active search of chemical space for better molecules, materials, and structures that improve battery durability and performance. The project will rely on computational data like molecular dynamics and reaction kinetics simulations done with quantum mechanical calculations as well as high throughput robotic electrochemistry experiments. We will explore Bayesian learning and those operating on graph-structured data as well as other approaches.

    The projects will be carried out in close collaboration with external partner groups and linked to other ongoing projects in the Section working on clean energy materials and machine learning for accelerated materials discovery.

    How to Apply?

    To apply, please open the link “Apply online,” fill in the online application form, and attach all your materials in English in one pdf file.

    Documents Required

    • A letter motivating the application (cover letter)
    • Curriculum vitae
    • BSc/MSc/PhD diploma
    • List of publications indicating scientific highlights
    • List of references (at least two)

    About Department of Energy Conversion and Storage

    The Department of Energy Conversion and Storage is focusing on functional materials and their application in sustainable energy technology. Our research areas include fuel cells, electrolysis, solar cells, electromechanical converters, sustainable synthetic fuels, and batteries. The Department, which has more than 200 employees, was founded in 2012. Additional information about the department can be found on www.energy.dtu.dk

    Note

    • The appointment will be for two years and be based on the collective agreement with the Confederation of Professional Associations. The allowance will be agreed upon with the relevant union.
    • If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark.

    Inquiries

    Prof. Tejs Vegge
    Email: teve@dtu.dk

    Dr. Arghya Bhowmik
    Email: arbh@dtu.dk.

    Please do not send applications to these e-mail addresses; instead, apply online as described above.

    Official advertisement

    Postdoc position in Applied Machine Learning for Accelerated Battery Innovation

    Post expires at 8:59am on Monday June 28th, 2021 (GMT+9)