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PhD Fellowship in Machine Learning for Modelling Atomic Structure – UCPH, Denmark Nov 2021


The University of Copenhagen invites applications for a Ph.D. scholarship in Machine Learning for Modelling Atomic Structure, will be employed within the AIchemy project – Computational Chemistry at Department of Computer Science, Faculty of Science, Denmark – Nov 2021

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General Info

Position: PhD Position
No. of Positions: 1
Research Field: , , ,
Deadline to Apply: Expired on
Joining Date: Feb 01, 2022
Contract Period: 3 Years
Salary: According to Standard Norms

Faculty of Science
Department of Computer Science


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Qualification Details

Applicants should hold an MSc degree in Computer Science, Engineering, Statistics or equivalent areas with strong Machine Learning foundation. The applicant must demonstrate good English skills. As criteria for the assessment of your qualifications, emphasis will also be laid on previous publications (if any) and relevant work experience.

Qualifications needed

To be eligible for the regular PhD programme, you must have completed a degree programme, equivalent to a Danish master’s degree (180 ECTS/3 FTE BSc + 120 ECTS/2 FTE MSc) related to the subject area of the project, e.g. Computer Science, Engineering, Statistics or equivalent areas. For information of eligibility of completed programmes, see General assessments for specific countries and Assessment database.

Responsibilities and tasks

  • Carry through an independent research project under supervision
  • Complete PhD courses corresponding to approx. 30 ECTS / ½ FTE
  • Participate in active research environments, including a stay at another research institution, preferably abroad
  • Undertake teaching and knowledge dissemination activities
  • Write scientific papers aimed at high-impact journals
  • Write and defend a PhD thesis on the basis of your project

We are looking for the following qualifications:

  • Professional qualifications relevant to the PhD project
  • Relevant publications
  • Relevant work experience
  • Other relevant professional activities
  • Curious mind-set with a strong interest in machine learning
  • Good language skills

Responsibilities/Job Description

The student will be employed within the AIchemy project. The aim is to develop machine learning methods with a focus on generative deep learning for analysis of synchrotron X-ray scattering data for structural characterization of nanomaterials. The project is expected to broadly influence the way material characterization with scattering methods is done through close collaborations between groups in computer science and chemistry. We will collaborate closely with chemistry researchers, in particular Associate Professor Kirsten Marie Ørnsbjerg Jensen and the chemistry PhD student also employed for the AIchemy project. Methodologically, the PhD student will likely contribute to

  1. Modelling the atomic structures using graphs and processing them using graph neural networks.
  2. Exploring latent variable models that capture a chemically meaningful embedding space, using variational inference and normalizing flows.
  3. Capturing symmetries in rotation, translations, reflections and permutations using theories from group equivariances.

How to Apply?

Online Application through "Apply Now" Button from this page

Reference Number: -
(If any, use it in the necessary place)

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Documents Required

Your application including all attachments must be in English and submitted electronically by clicking APPLY NOW below.

Please include:

  1. Cover Letter, detailing your motivation and background for applying for the specific PhD project(max. one page)
  2. Curriculum vitae including information about your education, experience, language skills and other skills relevant for the position
  3. Original diplomas for Bachelor of Science or Master of Science and transcript of records in the original language, including an authorized English translation if issued in another language than English or Danish. If not completed, a certified/signed copy of a recent transcript of records or a written statement from the institution or supervisor is accepted.
  4. Publication list (if possible)
  5. Reference letters (if available)

About the Research

The student will be employed at the Department of Computer Science ( in the Machine Learning Section and be part of the large group of theoretical and applied machine learning researchers. The student will also have a formalized collaboration with the Department of Chemistry at the University of Copenhagen. Another PhD scholarship is offered focusing on the atomic structure of nanomaterials. This setup offers world-class collaboration opportunities regarding the methodological as well as the inter-disciplinary aspects of the project.We offer creative and stimulating working conditions in a dynamic and international research environment.

Principal supervisor is Professor Erik Dam, Department of Computer Science, erikdam@di.ku.dk

Co-supervisors are Assistant Professor Raghavendra Selvan from the Department of Computer Science and Associate Professor Kirsten Marie Ørnsbjerg Jensen from Department of Chemistry.

The PhD programmeThe position is offered as a three year full-time study within the framework of the regular PhD programme (5+3 scheme) which requires that you already have an education equivalent to a relevant Danish master’s degree.

About the Employer: University of Copenhagen (UCPH)


Application deadline:

The deadline for applications is 7 November 2021 23:59 GMT +1.

We reserve the right not to consider material received after the deadline, and not to consider applications that do not live up to the abovementioned requirements.

The further process

After deadline, a number of applicants will be selected for academic assessment by an unbiased expert assessor. You are notified, whether you will be passed for assessment.

The assessor will assess the qualifications and experience of the shortlisted applicants with respect to the above mentioned research area, techniques, skills and other requirements. The assessor will conclude whether each applicant is qualified and, if so, for which of the two models. The assessed applicants will have the opportunity to comment on their assessment. You can read about the recruitment process at

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Contact details

For specific information about the PhD fellowship, please contact the principal supervisor.

General information about PhD study at the Faculty of SCIENCE is available at the PhD School’s website:

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