Postdoc in Machine Learning for Combating Antimicrobial Resistance

Antimicrobial resistance (AMR) is a major threat to global public health, causing millions of deaths each year. We are seeking a postdoctoral researcher to develop machine learning and generative modelling methods to design resistance-proof antibiotics.

You will join an interdisciplinary team, integrating machine learning, medicinal chemistry and microbiology. You will work with Asst. Prof. Eli N. Weinstein (DTU Chemistry) to develop novel molecular ML methods and use them to control, steer, and learn from large scale wetlab experiments. You will deploy these methods in collaboration with Prof. Katrine Qvortrup (DTU Chemistry) and Prof. Tim Tolker-Nielsen (UCPH Immunology & Microbiology).

The goal is to create an integrated AI and experimental system capable of anticipating and preempting the evolution of antibiotic resistance. You will build generative protein models to predict plausible future resistance mutations, use these models to guide high-throughput experimental screens of millions of enzyme variants, and train deep learning models on the resulting data to design new antibiotic compounds that evade both current and likely future resistance mechanisms. Your computational work will directly steer wetlab experiments carried out by collaborators in chemistry and microbiology, and you will use their experimental results to iteratively refine your models.

Responsibilities and qualifications
You will have responsibility for all computational aspects of the project, including:

• Developing and training generative protein models to predict plausible current and future resistance variants.
• Designing large scale DNA libraries with generative synthesis models, translating in silico predictions into physical gene libraries for experimental testing.
• Building and evaluating probabilistic deep learning models to predict bacterial growth from protein sequence and compound molecular structure.
• Applying generative and predictive models to propose novel compounds optimized for broad-spectrum activity against diverse resistance mechanisms.
• Collaborating closely with experimental partners to design screens, interpret results and iterate on model–experiment cycles.
• Publishing results in leading venues and presenting at international conferences.

You hold a PhD in computational biology/chemistry, machine learning or a related quantitative field. You have a solid publication record and demonstrated experience with advanced machine learning for biological or molecular data. We are looking for candidates with:

• Strong skills in probabilistic modelling, deep learning and/or generative modelling.
• Strong skills in computational biology, molecular machine learning or related areas.
• Enthusiasm for interdisciplinary collaboration and a genuine curiosity about experimental biology and chemistry.
• Excellent scientific communication skills, both written and oral.

You should be willing to take an ambitious and unconventional approach to hard problems and be motivated by a desire for both deep understanding and real-world impact.

Why this project

• High societal impact: Your work will contribute to the global fight against antimicrobial resistance, a crisis projected to cause 10 million deaths annually by 2050.
• Cutting-edge methods: You will work at the intersection of generative AI, molecular machine learning and ML-driven experimental design.
• Tight wet-lab integration: You will work closely with chemists and microbiologists, seeing your models translated into real experiments and new molecules.
• Collaborative environment: You will be part of an active consortium with regular cross-disciplinary discussion.

As a formal qualification, you must hold a PhD degree (or equivalent).

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 2 years. Start date of July 1, 2026 or a mutually agreed alternative. The position is full time.
You can read more about career paths at DTU here .

Further information
Further information may be obtained from Asst. Prof. Eli N. Weinstein enawe@dtu.dk
You can read more about the Chemistry Department at www.kemi.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 .

Application procedure
Your complete online application must be submitted no later than 31 May 2026 (23:59 Danish time).
Apply at: Postdoc in Machine Learning for Combating Antimicrobial Resistance

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
• Research sample. Please attach a key publication/preprint that you consider representative of your abilities. Provide a brief explanation (roughly 2-3 sentences) explaining your contributions.
• Contact information for two references.

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.

The Department of Chemistry performs research, educational activities, and innovation at a high international level within e.g. energy, catalysis, and materials. The overall aim is to contribute with new knowledge about basic scientific problems and carry out research with vast application perspectives - to the benefit of both society and companies.

DTU – For the benefit of society since 1829
DTU is one of Europe's leading elite technical universities. Through research and education at an international top level, we create solutions to the major societal challenges of our time and help secure Europe's global leadership in sustainable technological development. Since Hans Christian Ørsted founded DTU almost 200 years ago, our mission has remained the same: We develop and create value through the natural and technical sciences for the benefit of society. DTU has 13,800 students, 1,600 PhD students, and 6,500 employees. We work in an international environment and have an inclusive, stimulating, and informal work culture. DTU has campuses in all parts of Denmark and in Greenland and collaborates with the best universities around the world.