PhD scholarship in T Cell Immunoinformatics

Website Technical University of Denmark

The Section of Bioinformatics, DTU Health Tech is world leading within Immunoinformatics and Machine-Learning. Currently, we are seeking a highly talented and motivated PhD student within the field of T Cell Immunoinformatics.

Responsibilities and qualifications
The immune system defends us against pathogens through immune cells specialized to mount precise and individualized responses. The dynamic behavior of the immune cell repertoire vitally determines health and disease outcomes. A key driver of this dynamism is the interaction between T cell receptors (TCR) and pathogen proteins.

Considerable efforts have been devoted to developing computational models that predict TCR interactions. Despite these advances, predictive performance remains limited, and accurately characterizing TCR recognition continues to be a major challenge. We hypothesize that this is due to three interconnected factors: (i) the immense diversity of TCRs, (ii) the limited quantity and quality of available data, which is heavily biased toward a small number of well-studied epitopes, and (iii) the lack of deep learning (DL) approaches specifically designed to capture the unique characteristics of TCR interactions.

As part of the Deep Immune Receptor Modeling (DIRM) project, funded through the Novo Nordisk Foundation Data Science Collaborative Research Programme, this PhD project aims to address these challenges by developing advanced deep learning methods tailored to immune receptor biology. The project will integrate prior biological knowledge linking sequence and function and leverage active learning and data denoising strategies to iteratively identify and generate highly informative experimental data for model refinement.

The outcome will be a new generation of deep learning tools specifically optimized for immune receptor interactions. By advancing the application of artificial intelligence in immunoinformatics, the project will contribute to more accurate predictive models with significant potential for immunological discovery and clinical translation.

If you are seeking an ambitious and interdisciplinary PhD project that provides an outstanding foundation for a scientific career, while enabling you to contribute to cutting-edge research at the interface of immunology, data science, and machine learning, this position may be the opportunity you have been looking for.

The project will be conducted in the inspiring environment at the Section of Bioinformatics at DTU Health Tech as part of the IML research group led by Professor Morten Nielsen.

You must have a two-year master’s degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master’s degree.

You must moreover exhibit the following professional and personal qualifications: 

  • Strong background within machine learning/deep learning, and immunoinformatics is a requirement
  • Knowledge of structural biology and protein structure modeling is a plus
  • Knowledge of the basic concepts of the cellular immune system is a plus
  • Capability of taking personal responsibility for your work and your results 
  • Flexibility and a general positive attitude to changes 
  • Motivation by both individual and team accomplishments
  • Strong communication skills in both written and verbal English

Approval and Enrolment 
The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU’s rules for the PhD education

We offer
DTU is a leading technical university globally recognized for the excellence of its research, education, innovation and scientific advice. We offer a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility.

Salary and appointment terms
The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. “Please see DTU’s salary structure for scientific
staff: www.inside.dtu.dk/en/human-resources/during-employment/salary/salary-structures

The period of employment is 3 years with starting date of 1 November 2026 or as soon as possible hereafter. 

You can read more about career paths at DTU here.

Further information 
Further information may be obtained from Morten Nielsen, morni@dtu.dk and at Immunoinformatics and Machine Learning (IML).

You can read more about DTU Health Tech at https://www.healthtech.dtu.dk/ 

If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark. Furthermore, you have the option of joining our monthly free seminar “PhD relocation to Denmark and startup “Zoom” seminar” for all questions regarding the practical matters of moving to Denmark and working as a PhD at DTU. 

Application procedure 
Your complete online application must be submitted no later than 25 September 2026 (23:59 Danish time)

Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link “Apply now”, fill out the online application form, and attach all your materials in English in one PDF file. The file must include:

  • A letter motivating the application (cover letter)
  • Curriculum vitae 
  • Grade transcripts and BSc/MSc diploma (in English) including official description of grading scale

You may apply prior to ob­tai­ning your master’s degree but cannot begin before having received it.

Applications received after the deadline will not be considered.

All interested candidates irrespective of age, gender, disability, race, religion or ethnic background are encouraged to apply. As DTU works with research in critical technology, which is subject to special rules for security and export control, open-source background checks may be conducted on qualified candidates for the position.

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