项目介绍

2026年4月22日

Join our team at the Division of Molecular Bioscience, Department of Life Sciences, as Doctoral student in the Data-driven Life Science (DDLS) program!

Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes to human health and global ecosystems. The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) aims to recruit and train the next generation of data-driven life scientists and to create globally leading computational and data science capabilities in Sweden. The program is funded with a total of 3.3 billion SEK over 12 years from the Knut and Alice Wallenberg (KAW) Foundation.

In 2026 the DDLS Research School will be expanded with the recruitment of 25 academic and 7 industrial PhD students. During the course of the DDLS program more than 260 PhD students and 200 postdocs will be part of the Research School. The DDLS program has four strategic research areas: cell and molecular biology, evolution and biodiversity, precision medicine and diagnostics, epidemiology and biology of infection. For more information, please see https://www.scilifelab.se/data-driven/ddls-research-school/.The future of life science is data-driven. Will you be part of that change? Then join us in this unique program! 

At the Department of Life Sciences, we are announcing the positions as DDLS Doctoral student in Data-driven epidemiology and biology of infection.

Data driven epidemiology and biology of infection covers research that will transform our understanding of pathogens, their interactions with hosts and the environment, and how they are transmitted through populations. Research will have a strong focus on computational analysis or predictive modelling of pathogen biology or host-microbe systems for which multidimensional, genome-scale experimental data are now available or it may use population-scale genetic, clinical, or public health data from pathogen surveillance efforts and biobanks.

About us 

The Department of Life Sciences conducts research, innovation, and education to enable a biobased society and improve human health. We explore how biological systems, and innovative technologies can be used to convert biomass into valuable products. We use advanced computational technologies to discover how biomolecules and organisms function and interact. We pioneer new methods for prediction, prevention, diagnostics and treatment of diseases. 

In the division of Molecular Bioscience, we combine and develop protein engineering, synthetic chemistry, nucleic acid technology, and state-of-the-art biophysical methods (spectroscopy, live cell imaging and nanotechnology) to address cutting-edge questions in biology and meet medical challenges. Our joint expertise is strategic and spans from molecular biophysics of single molecules to cell and organism biology.

The Doctoral student will be part of the Antibiotic Research Group led by Michaela Wenzel, who will be the main supervisor of the thesis. The student will also be integrated into the Computational Systems Medicine Group led by Annikka Polster, who will serve as co-supervisor. The student will have a genuine computational home in both groups, with senior mentorship in experimental infection biology and in computational medicine.

About the research project  

In the project Towards a mechanism-aware multi-label prediction framework for antimicrobial peptides, the Doctoral student will use mechanistic data on antimicrobial peptides (AMPs) to connect structure not only to activity but also to function. The core challenge is a data and modelling problem: how do you build a predictive framework when the training labels themselves are systematically wrong?

AMPs are potent molecules occurring across all domains of life, with activities ranging from broad-spectrum antibacterial and antibiofilm action to antifungal, antiviral, antiparasitic, and immunomodulatory properties. Their low propensity to generate microbial resistance has made them promising antibiotic alternatives, with several already in clinical use. Yet for decades, AMP mechanisms of action were oversimplified to generic membrane disruption, a picture that has since been shown to misrepresent how most AMPs actually work in living systems. The result is a literature landscape full of mislabelled data that limits the reliability of any data-driven approach built on it.

Our team has access to three unique curated datasets combining multi-omics, microscopy, and spectroscopy data, designed specifically to capture the full mechanistic complexity of AMP action on microbial cells. These provide an unusually clean foundation for a genuinely hard machine learning problem: multi-label classification across mechanistically heterogeneous biological classes, where the label space itself must be carefully constructed.

The Doctoral student’s work will centre on ontology-informed dataset integration, multi-label model development, systematic benchmarking, and biological validation. The methodological space spans structured data integration, representation learning, and multi-label classification. The primary work is computational. Candidates who wish to engage in experimental validation work will have the opportunity to do so during model validation; it is not a requirement. 

Who we are looking for

The following requirements are mandatory:

• To qualify as a Doctoral student, you must have a Master’s degree (masterexamen) of 120 credits or a Master’s degree (magisterexamen) of 60 credits* in a life science or data science-related discipline. 
• Experience in data science, computational biology/medicine, bioinformatics, AI, or related areas
• Programming skills (if not at the time of application, you must obtain proof of sufficient skills until the start of the project)
• Strong written and verbal communication skills in English 
• A highly interdisciplinary and collaborative mindset
• An excellent ability to work and communicate across disciplines

*for students with an education earned outside of Sweden, a 4-year Bachelor’s degree is accepted. 

The following experience will strengthen your application: 

• The ideal candidate has a background in data-driven life science, bioinformatics, AI for science, or another field that brings together computational and life sciences.
• Candidates with a strong ML or data science background and limited prior bioscience experience are encouraged to apply; the necessary biological knowledge can be acquired through PhD coursework.
• Candidates without significant computational background will only be considered in exceptional cases.

What you will do

• Develop an integrated mechanistic database through ontology-informed curation and standardisation of multi-omics, microscopy, and spectroscopy data
• Design, develop, benchmark, and validate AI models for multi-label prediction of AMP mechanisms of action
• Take courses at an advanced level within the Graduate school of Bioscience and the DDLS research school
• Develop your own scientific concepts and communicate the results of your research verbally and in writing 
• The position generally also includes teaching on Chalmers’ undergraduate level or performing other duties corresponding to 20 percent of working hours

Contract terms  

• The Doctoral student positions are fully funded from the start. 
• The position is a fixed-term appointment of four years, with the possibility to teach up to 20%, which extends the position up to five years. 
• A starting salary of 34,550 SEK per month (valid from May 25, 2025). 
• Doctoral studies require physical presence throughout the entire study period. A valid residence permit must be presented by the study start date; otherwise the admission may be withdrawn. 

What we offer 

• As a Doctoral student at Chalmers, you are an employee and enjoy all employee benefits. Read more about  working at Chalmers  and our benefits for employees. 
• A dynamic and inspiring working environment in the coastal city of Gothenburg. 
• Read more about Sweden’s generous parental leave, subsidized day care, free schools, healthcare etc at Move To Gothenburg. 

Chalmers is dedicated to improving gender balance and actively works with equality projects, such as the GENIE Initiative for gender equality and excellence. We celebrate diversity and consider equality and inclusion as fundamental aspects of all our activities. 

If Swedish is not your native language, Chalmers offers Swedish courses to help you settle in.

Find more general information about doctoral studies at Chalmers here.  

Application procedure 

The application should be written in English and attached as PDF-files, as below. Maximum size for each file is 40 MB. Please note that the system does not support Zip files. 

CV 

Personal letter 

• A brief introduction about yourself. 
• A brief motivation as to why you are interested in this position. 

Bachelor’s and, if available, master’s thesis together with the transcripts. 

Use the button at the foot of the page to reach the application form.  

A background check may be conducted as part of the application process.

Please note: The applicant is responsible for ensuring that the application is complete. Incomplete applications and applications sent by email will not be considered. Contact details to references will be requested after the interview. 

We welcome your application no later than 31 May, 2026.

Interviews are expected to take place in June. The planned starting date of the PhD is October 2026. 

For questions, please contact:  

Michaela Wenzel
Associate Professor
wenzelm@chalmers.se

We look forward to your application!