cAIre tech

Target group

This is a project for PhD students (1 month) ( Possible to welcome up to 6 students).

About the company

cAIre tech is developing an AI-powered decision support platform that helps healthcare practitioners reverse chronic diseases through evidence-based lifestyle and nutrition recommendations informed by biochemistry, systems biology, and epigenetics.

Conventional care typically treats diseases as separate problems rather than as parts of an interconnected biological system. In contrast, systems biology — often referred to as Functional Medicine — seeks to understand how and why illness occurs by addressing root causes unique to each individual. This approach is also practiced at institutions such as the Cleveland Clinic and emphasizes personalized, physiology-based care grounded in biochemistry and lifestyle science.

The project focuses on designing an explainable AI framework to identify biological root causes of chronic diseases using multimodal and longitudinal health data. The model will integrate genetic data, laboratory biomarkers, symptom patterns, medical history, and lifestyle factors into interconnected physiological system maps (e.g., metabolic, immune, hormonal). It will combine machine learning and other AI models to distinguish upstream drivers from downstream manifestations. Synthetic data generation will be used to augment sparse datasets, simulate biological scenarios, and validate robustness while preserving privacy. The architecture will be designed for federated learning to enable secure, distributed development across clinical sites.

The expected outcome is a clearly defined and documented modeling framework capable of generating structured root-cause hypotheses and system imbalance mappings at an individual level. The project will deliver a prototype architecture describing data schemas, feature engineering logic, modeling strategy, and federated training design. Synthetic data pipelines will support validation, bias testing, and scenario analysis. Evaluation metrics will prioritize interpretability, biological plausibility, and clinical relevance. This will establish a scalable and research-ready foundation for root-cause decision support in chronic disease care.

Location

The selected candidates will mainly be working remotely, and sometimes from Knowit Connectivity’s office in Kista.

Time period - The internship is full-time for 4 weeks, with the possibility of extending it on a part-time basis if preferred. The start date is flexible and can be as early as possible. Ideally, we aim to engage multiple interns who can work either in parallel or sequentially between May and December 2026.

The candidate(s)

We are seeking PhD candidates with strong technical expertise and a deep interest in AI in healthcare.

Required Background

• Ongoing PhD in AI, data science, biomedical engineering, bioinformatics, systems biology, computational medicine, or related field.
• Experience working with clinical or biomedical datasets
• Familiarity with research design and scientific validation

Contact Information

Name: Marie Andersson 

Email: marie.andersson@cairetech.com

Telephone: +46 7676868620

Company website: www.cairetech.com