Department of Computer Science

Faculty of Science

University of Copenhagen

A PhD fellowship is available in the group of Professor Anders Krogh (https://scholar.google.com/citations?user=-vGMjmwAAAAJ). The group focusses on machine learning and bioinformatics and in particular development of deep generative models and applications to medical data. We are seeking people to do research on one or more of these topics:

1. Development of deep generative neural networks. We are seeking people with a good theoretical background, who can also work on implementations and applications.
2. Applications of generative models to medical data such as bulk and single-cell gene expression data and clinical data.

We seek a highly motivated person that can contribute to the theoretical and applied work in the group. The group is part of “Center for Basic Machine Learning Research in Life Science” (mlls.dk), which is a lively crowd of six PIs and around 30 PhD students and postdocs. We are associated with the Pioneer Centre for AI (aicentre.dk) and have regular seminars, conferences and a popular yearly retreat. The principal supervisor will be Anders Krogh, but PhD students will generally have a co-supervisor from another group in MLLS and is expected to also work with other people in the Center. We are also collaborating with several foreign research groups.

Further information on the Department is linked at https://www.science.ku.dk/english/about-the-faculty/organisation/. Inquiries about the positions can be made to Anders Krogh, akrogh@di.ku.dk.

The PhD programme

A three year full-time study within the framework of the regular PhD programme (5+3 scheme), if you already have an education equivalent to a relevant Danish master’s degree.

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) in computer science/engineering, bioinformatics, mathematics, physics or a closely related field. For information of eligibility of completed programmes, see General assessments for specific countries and Assessment database.

Additionally you are expected to

• Have a strong background in machine learning
• Have programming experience (Python) and experience with machine learning libraries, such as Pytorch
• Have a good understanding of math and statistics
• Some background in biology/medicine/biochemistry is an advantage
• Be fluent in English
• Be responsible and self-motivated

Terms of employment
Employment as PhD fellow is full time and for maximum 3 years. Start date is on 1 December 2023 or as soon as possible thereafter.

Employment is conditional upon your successful enrolment as a PhD student at the PhD School at the Faculty of SCIENCE, University of Copenhagen. This requires submission and acceptance of an application for the specific project formulated by the applicant.

Terms of appointment and payment accord to the agreement between the Danish Ministry of Taxation and The Danish Confederation of Professional Associations on Academics in the State. The position is covered by the Protocol on Job Structure.

Responsibilities and tasks in the PhD program

• 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
• 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

Application and Assessment Procedure

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

Please include:

1. Motivated letter of application (max. one page) including information about the field of interest (points 1-3 above)
2. Curriculum vitae including information about your education, experience, language skills and other skills relevant for the position
3. Original diplomas for 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 relevant)
5. Reference letters (if available)

Application deadline:

The deadline for applications is 3 September 2023, 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 https://employment.ku.dk/faculty/recruitment-process/.

Questions
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: https://www.science.ku.dk/phd/.

The University of Copenhagen wishes to reflect the surrounding community and invites all regardless of personal background to apply for the position. 

Part of the International Alliance of Research Universities (IARU), and among Europe’s top-ranking universities, the University of Copenhagen promotes research and teaching of the highest international standard. Rich in tradition and modern in outlook, the University gives students and staff the opportunity to cultivate their talent in an ambitious and informal environment. An effective organisation – with good working conditions and a collaborative work culture – creates the ideal framework for a successful academic career.