Postdoc in T Cell Immunoinformatics - DTU Health Tech at Danmarks Tekniske Universitet

Kongens Lyngby, Capital Region of Denmark, Denmark -

Full Time

Start Date

Immediate

Expiry Date

13 May, 26

Salary

0.0

Posted On

12 Feb, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Immunoinformatics, T Cell Immunogenicity Prediction, Bioinformatics, HLA Class II Antigen Presentation, T-Cell Activation Assays, Data Analysis, Scientific Communication, Research Independence, Protein Drug Development

Industry

Research Services

Description

The Section of Bioinformatics, DTU Health Tech is world leading within Immunoinformatics and Machine-Learning. Currently, we, together with a leading external pharma company party, are seeking a highly talented and motivated Postdoc candidate within the field of Immunoinformatics and prediction of T cell immunogenicity. HLA class II antigen presentation is fundamental to T-helper cell immunogenicity. Over the past decade, this process has been well characterized, and robust methods have been developed to predict it with high confidence. In contrast, our understanding of the principles governing T-cell immunogenicity of HLA-presented peptides remains limited. This gap significantly restricts the utility of current antigen presentation quantification tools—both in silico and experimental—hindering early immunogenicity risk assessment during protein drug development. A deeper understanding of the mechanisms driving T-cell activation would represent a paradigm shift in biologics development. However, progress is constrained by the scarcity of high-quality, well-characterized in vitro T-cell immunogenicity data. In this project, we will leverage expertise and technology in peptide T-cell immunogenicity with DTU’s strengths in immunoinformatics and machine learning, to address this challenge. Our work will provide novel insights into the current T-cell activation assays and datasets, and enable the development of the first predictive model for T-cell immunogenicity of HLA class II-presented peptides in the context of protein therapeutics. If you're looking to launch your scientific career at the intersection of bioinformatics and machine learning, this position supports a pathway toward academic excellence and research independence – whilst also offering a unique opportunity to gain hands-on experience with a global pharmaceutical leader. Responsibilities and qualifications 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. Your main focus will be centered on further developing the competences of the IML group within prediction of T cell immunogenicity and immunoinformatics in general. You will work with nearby bioinformatics, postdocs and PhD students working on other projects within the group and experimental immunology colleagues. As a formal qualification, you must hold a PhD degree (or equivalent). The successful candidate must moreover exhibit the following professional and personal qualifications: Strong background within machine learning learning, and immunoinformatics is a requirement Knowledge of the basic concepts of the cellular immune system or a background in experimental immunology is desirable 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 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 terms of employment The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. The allowance will be agreed upon with the relevant union. The period of employment is 3 years with starting date of 1 May 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 www.healthtech.dtu.dk/english. If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark. Application procedure Your complete online application must be submitted no later than 15 March 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: Application (cover letter) CV Academic Diplomas (MSc/PhD – in English) List of publications 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. Section of Bioinformatics The section of Bioinformatics within DTU Health Tech has its main research and teaching activities in bio-medical and bio-technological informatics, metagenomics, epidemiology, integrative systems biology and machine learning. The research at DTU Bioinformatics is focused on bioinformatics and computational analyses of large amounts of data generated within biological, biomedical and biotechnological and life sciences area. We strive to gain new knowledge and drive innovation in human, animal, plant and food science and collaborate with all relevant industries. A cornerstone in achieving this is the dedicated effort within DTU Bioinformatics to develop bioinformatics, immuno-informatics, metagenomics and systems biology methods and solutions to the challenge of handling and interpreting large scale and heterogeneous big data. DTU Health Tech DTU Health Tech engages in research, education, and innovation base on technical and natural science for the healthcare sector. The Healthcare sector is a globally expanding market with demands for the most advanced technological solutions. DTU Health Tech creates the foundation for companies to develop new and innovative services and products which benefit people and create value for society. DTU Health Techs expertise spans from imaging and biosensor techniques, across digital health and biological modelling, to biopharma technologies. The department has a scientific staff of about 210 persons, 130 PhD students and a technical/administrative support staff of about 160 persons, of which a large majority contributes to our research infrastructure and related commercial activities. Technology for people DTU develops technology for people. With our international elite research and study programmes, we are helping to create a better world and to solve the global challenges formulated in the UN’s 17 Sustainable Development Goals. Hans Christian Ørsted founded DTU in 1829 with a clear mission to develop and create value using science and engineering to benefit society. That mission lives on today. DTU has 13,500 students and 6,000 employees. We work in an international atmosphere and have an inclusive, evolving, and informal working environment. DTU has campuses in all parts of Denmark and in Greenland, and we collaborate with the best universities around the world.

Responsibilities

The main focus will be centered on further developing the competences of the IML group within the prediction of T cell immunogenicity and immunoinformatics in general. The role involves working with nearby bioinformatics staff, postdocs, PhD students, and experimental immunology colleagues on developing predictive models for T-cell immunogenicity.