Postdoctorate Fellowships in Machine Learning or Quantum Computing
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Postdoctorate Fellowships in Machine Learning or Quantum Computing
The Department of Computer Science invites applicants for 24 month (with possibility for extension) postdoctoral fellowships in (i) Multi-Modal Representation Learning for Design of Sustainable Food Processing, and (ii) quantum and tensor network algorithms. The projects are distinct, and specific expertise is only expected in one of the two topics.
1. Multi-Modal Representation Learning for Design of Sustainable Food Processing The project is part of the larger research project “AI4NaturalFood”, in collaboration with the Department of Food Science, and the project is financed by the Novo Nordisk Foundation.
Start date is 1st February, 2026 or as soon as possible thereafter.
The project is about developing machine learning (ML) methods that help to develop the food of the future. The successful candidate is expected to conduct basic research in ML and to contribute to the application of ML in the food science.
Currently, plant ingredients are often refined to almost molecular purity - and then combined again to create structured foods. This isolation is resource intensive, and the removal of fibre and micronutrients can compromise the nutritional value. This can be mitigated by applying milder forms of processing that do not fully refine ingredients and leave some of the native structure of the plant material intact. These less refined ingredients however exhibit complex behaviour, and we therefore need machine learning to direct the experimental data generation that will be carried out by other team members in the project.
Learning meaningful representation spaces that model the complex space of ingredients and their properties can be immensely useful. These representation spaces can be informed by multiple modalities of data, spanning time-series data, microscopy images, rheological measurements, and so on. Integrating these modalities into common representation spaces can help in the development of more sustainable food. Furthermore, incorporating active learning methods can steer new experimentation while incorporating adequate prior domain knowledge.
Who are we looking for?
We are looking for candidates with a PhD degree and demonstrable experience in the field of machine learning with a focus on representation learning; additional experience in the food sector and especially in food processing would be strong plus.
Our group and research- and what do we offer?
We offer a pioneering position that is part of a large collaboration between several departments within the University of Copenhagen and with leading international universities in the field. The postdoctorate Fellow will be positioned at the Computer Science department (located at the Universitetsparken Campus) but also be part of a growing group at the Food Science department (located at the Frederiksberg Campus) that will set up a significant effort on generating the data sets to be used in conjunction with the modelling. The postdoc is expected to bridge these two areas.
Supervisors are Prof Remko Boom Remko.boom@food.ku.dk, Prof Christian Igel igel@di.ku.dk and Tenure-track Assistant Professor Raghavendra Selvan raghav@di.ku.dk.
Responsibilities and tasks
We are looking for candidates with a PhD degree and demonstrable experience in the field of quantum computing and quantum information, with a focus on either tensor network methods, or quantum algorithms, or quantum error correction. The candidate must have a strong track record in one of the above topics and have a clear research agenda that complements the activities in the group.
Our group and research- and what do we offer?
Our group is currently composed of one PI (Kastoryano), one postdoc, 6 PhD students, one research assistant, and various master students at any point in time. We prioritize collaborative work across several topics and several institutes, with especially close collaboration with the Niels Bohr institute (nbi.ku.dk/) and the Novo Nordisk center for quantum computing (nqcp.ku.dk/). The research in the group includes the following topics:
Responsibilities and tasks
The candidate will primarily be a member of the research group Foundations of Responsible Machine Learning, led by Amartya Sanyal in the Department of Computer Science, as well as the broader DeLTA lab in the Machine Learning Section.
Our section and research - and what do we offer?
The Machine Learning Section is a part of the Department of Computer Science, Faculty of SCIENCE, University of Copenhagen and the ELLIS Unit Copenhagen (https://ellis.eu/). The department is heading two centers within Artificial Intelligence: the SCIENCE AI Center and the Pioneer Center within Artificial Intelligence.
Your job
The hired candidate is expected to both lead independent research within topics of responsible machine learning as well as collaborate with other members of the research group. In addition, the candidate is also expected to regularly publish in top tier conferences in their domain including but not limited to NeurIPS, ICML, ICLR, COLT, ALT, FORC, STOC, FOCS, AISTATS, etc.
The postdoc’s responsibilities will primarily consist of:
Important: When applying, please write down Christian Igel (Position 1), Michael Kastoryano (Position 2), or Amartya Sanyal (project 3) in the Principal Supervisor field to indicate which supervisor you are applying to. The Principal Supervisor indicated by you will review your application. If relevant, you can express interest in both projects in your cover letter and we will consider it internally. Applications that have not indicated a Principal Supervisor will not be reviewed.
Application and Assessment Procedure
Your application including all attachments must be in English and submitted electronically by clicking APPLY NOW below.
Please include:
The deadline for applications is 14 December 2025, 23.59 CET.
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 http://employment.ku.dk/faculty/recruitment-process/.
Questions
For specific information about the fellowship, please contact the principal supervisors.
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.
1. Multi-Modal Representation Learning for Design of Sustainable Food Processing The project is part of the larger research project “AI4NaturalFood”, in collaboration with the Department of Food Science, and the project is financed by the Novo Nordisk Foundation.
Start date is 1st February, 2026 or as soon as possible thereafter.
The project is about developing machine learning (ML) methods that help to develop the food of the future. The successful candidate is expected to conduct basic research in ML and to contribute to the application of ML in the food science.
Currently, plant ingredients are often refined to almost molecular purity - and then combined again to create structured foods. This isolation is resource intensive, and the removal of fibre and micronutrients can compromise the nutritional value. This can be mitigated by applying milder forms of processing that do not fully refine ingredients and leave some of the native structure of the plant material intact. These less refined ingredients however exhibit complex behaviour, and we therefore need machine learning to direct the experimental data generation that will be carried out by other team members in the project.
Learning meaningful representation spaces that model the complex space of ingredients and their properties can be immensely useful. These representation spaces can be informed by multiple modalities of data, spanning time-series data, microscopy images, rheological measurements, and so on. Integrating these modalities into common representation spaces can help in the development of more sustainable food. Furthermore, incorporating active learning methods can steer new experimentation while incorporating adequate prior domain knowledge.
Who are we looking for?
We are looking for candidates with a PhD degree and demonstrable experience in the field of machine learning with a focus on representation learning; additional experience in the food sector and especially in food processing would be strong plus.
Our group and research- and what do we offer?
We offer a pioneering position that is part of a large collaboration between several departments within the University of Copenhagen and with leading international universities in the field. The postdoctorate Fellow will be positioned at the Computer Science department (located at the Universitetsparken Campus) but also be part of a growing group at the Food Science department (located at the Frederiksberg Campus) that will set up a significant effort on generating the data sets to be used in conjunction with the modelling. The postdoc is expected to bridge these two areas.
Supervisors are Prof Remko Boom Remko.boom@food.ku.dk, Prof Christian Igel igel@di.ku.dk and Tenure-track Assistant Professor Raghavendra Selvan raghav@di.ku.dk.
Responsibilities and tasks
- Developing ML method for representation learning that allow to map the space of food ingredients and functional properties
- Developing methods for active learning that can process data collected by other team members and using this to steer further data collection
- Participate in a larger, multidisciplinary team of postdocs and PhD students
- Coordinate a part of the larger project together with the PIs
- Co-supervise PhD student(s) within the larger project, together with the PIs
- Write scientific papers aimed at high-impact journals
- A PhD degree in a relevant field (machine learning or equivalent computer sciences)
- Experience in machine learning applied in the field of food technology and processing is a plus.
- An inquisitive mind-set with a strong interest in exploring new ways of experimentation and a willingness to work with people having different scientific backgrounds
- Good language skills in English (the professional language of communication); familiarity with the Danish language is not required but a plus.
We are looking for candidates with a PhD degree and demonstrable experience in the field of quantum computing and quantum information, with a focus on either tensor network methods, or quantum algorithms, or quantum error correction. The candidate must have a strong track record in one of the above topics and have a clear research agenda that complements the activities in the group.
Our group and research- and what do we offer?
Our group is currently composed of one PI (Kastoryano), one postdoc, 6 PhD students, one research assistant, and various master students at any point in time. We prioritize collaborative work across several topics and several institutes, with especially close collaboration with the Niels Bohr institute (nbi.ku.dk/) and the Novo Nordisk center for quantum computing (nqcp.ku.dk/). The research in the group includes the following topics:
- Tensor network algorithms for partial differential equations,
- Tensor network algorithms for computational finance,
- Tensor network and quantum algorithms for machine learning
- Open system simulation algorithms
- Quantum algorithms for many-body simulation, including Gibbs sampling.
- Quantum error correction, with a focus on decoding algorithms and quantum self-correction
Responsibilities and tasks
- Developing a research portfolio on topics adjacent to the group, and contribute to existing PhD research projects
- Contribute to open source scientific software projects, typically involving tensor network methods.
- Coordinate workshops and seminars together with the PI
- Co-supervise PhD student(s) within the larger project, together with the PIs
- Write scientific papers aimed at high-impact journals
- Good language skills in English (the professional language of communication); familiarity with the Danish language is not required but a plus.
- A PhD degree in a relevant field (theoretical computer science, physics, mathematics or related fields)
- Experience in tensor network methods, or quantum algorithms, or quantum error correction.
- An inquisitive mind-set with a strong interest in exploring new ways of experimentation and a willingness to work with people having different scientific background.
The candidate will primarily be a member of the research group Foundations of Responsible Machine Learning, led by Amartya Sanyal in the Department of Computer Science, as well as the broader DeLTA lab in the Machine Learning Section.
Our section and research - and what do we offer?
The Machine Learning Section is a part of the Department of Computer Science, Faculty of SCIENCE, University of Copenhagen and the ELLIS Unit Copenhagen (https://ellis.eu/). The department is heading two centers within Artificial Intelligence: the SCIENCE AI Center and the Pioneer Center within Artificial Intelligence.
Your job
The hired candidate is expected to both lead independent research within topics of responsible machine learning as well as collaborate with other members of the research group. In addition, the candidate is also expected to regularly publish in top tier conferences in their domain including but not limited to NeurIPS, ICML, ICLR, COLT, ALT, FORC, STOC, FOCS, AISTATS, etc.
The postdoc’s responsibilities will primarily consist of:
- research, including publication/academic dissemination duties at top tier conferences listed above
- research-based teaching
- sharing knowledge with society
- You have a PhD in Computer Science, mathematics, or statistics
- You are highly experienced in theoretical topics within machine learning
- You have an active interest in topics within responsible machine learning
- Proficient communication skills and ability to work in teams
- Excellent English skills written and spoken
Important: When applying, please write down Christian Igel (Position 1), Michael Kastoryano (Position 2), or Amartya Sanyal (project 3) in the Principal Supervisor field to indicate which supervisor you are applying to. The Principal Supervisor indicated by you will review your application. If relevant, you can express interest in both projects in your cover letter and we will consider it internally. Applications that have not indicated a Principal Supervisor will not be reviewed.
Application and Assessment Procedure
Your application including all attachments must be in English and submitted electronically by clicking APPLY NOW below.
Please include:
- Motivated letter of application (max. one page), that describes your motivation for applying for the Fellowship
- Curriculum vitae including information about your education, experience, language skills and other skills relevant for the position
- Original diplomas for MSc and PhD 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.
- Full publication list
- Reference letters (if available)
The deadline for applications is 14 December 2025, 23.59 CET.
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 http://employment.ku.dk/faculty/recruitment-process/.
Questions
For specific information about the fellowship, please contact the principal supervisors.
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.