PhD in AI-Driven Cancer Prevention Using Digital Health and Nutrition Data

Develop scalable AI models to integrate app-collected health and nutrition data for cancer risk prediction and prevention.

Best for:Best for researchers with a master’s in biological sciences, computer science, or related fields, passionate about AI-driven public health and large-scale data integration.

Hector Fellow Academy · Dresden, Germany · R1 · First-stage researcher

More professors at Hector Fellow Academy

AI-driven cancer preventionDigital health data integrationNutrition and lifestyle risk analysisLarge-scale data analyticsScalable prevention modelsCancer incidence prediction

Deadline

Oct 15, 2026

0 days

Funding

—

Start date

flexible (expected soon after selection)

Full-time

Location

Dresden

Germany

Overview

This PhD position at Dresden University of Technology (TU Dresden) focuses on leveraging AI to analyze large-scale digital health and nutrition data for cancer prevention. The project aims to integrate app-collected lifestyle and metabolic data with structured food databases and local cancer incidence records. By identifying modifiable risk patterns, the research will create interpretable, scalable prevention models. The work is collaborative with the German Cancer Research Center (DKFZ) Dresden and targets innovative approaches to public health using computational methods. Applicants should have a strong background in biological sciences or computer science and proficiency in English.

AI in healthcareDigital health analyticsNutrition data scienceCancer prevention researchApp-collected health dataMachine learning for public healthData integration pipelinesRisk prediction models

What you'll do

Develop AI-driven algorithms to integrate app-collected digital health, lifestyle, and nutrition data with structured food and nutrition databases.
Analyze large-scale datasets to identify modifiable dietary, behavioral, and metabolic risk patterns linked to cancer prevention.
Design and implement scalable, interpretable prevention models using computational methods.
Collaborate with researchers at the German Cancer Research Center (DKFZ) Dresden to validate findings with local cancer incidence data.
Publish research findings in peer-reviewed journals and present results at international conferences.
Contribute to the development of data-driven strategies for public health interventions.

Requirements

Degree required: Master’s degree or equivalent in Biological Sciences, Computer Science, Data Science, or a related field

Degree offered: PhD

Language: English; excellent proficiency (verified by formal certification if required)

Instruction: English

Required skills

Proven expertise in AI, machine learning, or data science
Experience with large-scale data integration and analysis
Strong programming skills (e.g., Python, R, or equivalent)
Familiarity with digital health data collection and analysis tools
Knowledge of statistical modeling and risk prediction techniques

Nice to have

Experience with nutrition or public health data analysisBackground in biomedical research or epidemiologyFamiliarity with healthcare databases or electronic health recordsPublication record in relevant scientific journals

Eligibility

No restrictions on nationality or residency
Applicants must hold a master’s degree or equivalent at the time of application

Funding & terms

Type

Unknown

Amount

—

Duration

—

Programme

Hector Fellow Academy

How to apply

Deadline: 2026-10-15 (Europe/Berlin)

Applicants must submit their application through the Hector Fellow Academy’s official application portal. The process requires uploading a CV, cover letter, academic transcripts, and a research proposal. Three letters of recommendation are also required. Detailed instructions and the application form are available on the provided link.

Documents required