Industrial PhD Student in Data-Driven Modeling of Human Biology

About the role
We are looking for an Industrial PhD Student to join an ambitious project focused on building foundational models of human biology using large-scale, multimodal data.

In this role, you will work at the intersection of machine learning and life sciences, developing next-generation models that improve our understanding of human biology and enable more proactive, personalized healthcare.

As an Industrial PhD Student, you will be employed by ABC Labs while pursuing your PhD at KTH Royal Institute of Technology, giving you a unique opportunity to combine academic research with real-world impact.

Project overview
The project aims to develop large-scale self-supervised models trained on rich, longitudinal health data, including:

You will work with one of the most comprehensive multimodal datasets available, enabling research at the frontier of data-driven biology.

What you’ll do

• Develop and train large-scale machine learning models for biological data

• Work with multimodal and longitudinal health datasets

• Explore self-supervised and representation learning approaches

• Collaborate with domain experts in biology, medicine, and data science

• Contribute to advancing preventive and personalized healthcare

What we offer

• Access to unique longitudinal multimodal health data

• A highly interdisciplinary environment combining AI and life sciences

• Collaboration with experts in lab science, medicine, and machine learning

• Modern GPU compute infrastructure

• A chance to contribute to cutting-edge research with real-world impact

Who you are

• Strong background in Machine Learning, with the ability to understand and extend current research

• Solid programming and engineering skills

• Comfortable working with modern development tools and practices

• Strong communication and collaboration skills

Nice to have:

Curiosity and willingness to learn are highly valued.

Requirements

About the opportunity:
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 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

The future of life science is data-driven. Will you be part of that change? Then join us in this unique program!

The project is part of collaboration between ABC Labs & SciLifeLab. As an Industrial PhD Student you will be employed by ABC Labs and admitted as a PhD student at KTH, with Dr. Wei Ouyang (AICell Lab) as an academic supervisor. That gives you a unique opportunity to advance development of science, with access to expertise, resources, and competences of an accredited medical testing lab

Diversity & Inclusion
We are committed to building a diverse and inclusive workplace. We welcome applications from all qualified candidates regardless of gender, gender identity or expression, age, disability, ethnicity, religion or belief, sexual orientation, or any other protected characteristic under EU or local law.

Data Protection Notice
By applying for this role, you consent to the processing of your personal data in accordance with applicable data-protection laws (including GDPR) and our recruitment privacy policy.

Join our team to work with brilliant colleagues innovating at the forefront of diagnostics by creating technologies combining software, robotics, data science, medical review, lab testing, and customer-facing operations to enable end-to-end products covering the entire spectrum of user needs - building out an infrastructure for diagnostics that becomes exponentially more efficient over time.