IDEAS PhD Call 2026: 8 Interdisciplinary Projects in Data Science for Environmental & Life Sciences

The Helmholtz Centre for Environmental Research (UFZ) with its 1,100 employees has gained an excellent reputation as an international competence centre for environmental sciences. We are part of the largest scientific organisation in Germany, the Helmholtz association. Our mission: Our research seeks to find a balance between social development and the long-term protection of our natural resources.

IDEAS PhD Call 2026:

8 Interdisciplinary Projects in Data Science for Environmental & Life Sciences

The job

The Helmholtz School for Integrated Data Science in Environmental and Life Sciences (IDEAS) connects the domain-science expertise of UFZ and HZDR with the data/ information science strength of Leipzig University (LU) and TU Dresden (TUD), supported by CASUS as an interdisciplinary bridge. IDEAS is part of the Helmholtz Information & Data Science Schools under the Helmholtz Data Science Academy (HIDA).

Our research focus

IDEAS advances and applies modern data science to complex challenges in environmental and life sciences (e.g., machine learning, explainable AI, uncertainty quantification, and AI-ready FAIR data and research data management).

What you can expect at IDEAS

IDEAS offers structured, interdisciplinary supervision and training, including joint supervision across disciplines, a Thesis Advisory Committee (TAC), a tailored curriculum, and cohort activities (seminars, hackathons, retreats), plus strong career development and networking through the IDEAS/ HIDA ecosystem.

PhD topics

This collective call includes 8 PhD topics, of which 6 positions will be funded. Applicants can be considered for multiple projects and will be matched through a structured selection and ranking process.

Climate Disasters

Climate disasters cause major human and economic losses, but it remains difficult to explain why impacts differ across places and time. This PhD project combines newly available disaster, socio-economic, and satellite datasets with interpretable machine learning to disentangle the roles of hazard intensity, exposure, vulnerability, and environmental conditions. You will develop data‑science methods that address biased impact records and the spatio‑temporal structure of the data, with the goal of improving our understanding of what drives disaster impacts and making climate‑risk assessment more reliable.

Beyond Traditional Monitoring

Traditional chemical monitoring can miss short‑lived or poorly captured pollution events—yet these may be visible in newspapers, local reports of fish kills, or social media complaints about odours and discoloured rivers. This PhD project develops AI‑based methods to integrate such event signals with regulatory intelligence and chemical data in the SARDINE platform. The aim is to strengthen mixture risk assessment and improve freshwater protection by revealing monitoring blind spots and better characterising real‑world exposure.

Many proteins remain “functionally uncharacterised”

In this PhD project, you will use protein language models trained on billions of sequences to study how scale, and evolutionary and ecological diversity shape model generalisation. You will develop interpretable and robust embeddings and validate predictions with Helmholtz lab partners, ultimately enabling discovery of novel enzymes relevant for sustainable biotechnology.

Large language models rely heavily on internalised factual knowledge, but the true extent of that knowledge is hard to measure.

This PhD project reframes LLM knowledge as “knowledge diversity”, drawing an analogy to biodiversity in ecology, and applies ecological estimation methods to infer the amount of knowledge from limited samples. By bridging computer science and statistical ecology, the project aims to produce reliable knowledge estimates for LLMs while also stress‑testing and advancing biodiversity estimators at scale.

Agentic Cancer Care

This PhD project develops multimodal AI that integrates clinical text and medical imaging to reduce overdiagnosis, overtreatment, and unnecessary monitoring in prostate cancer—supporting better decisions for thousands of patients. You will combine foundation models and LLM‑based agents with large‑scale computing in an international team spanning Helmholtz (Germany) and Danish clinical and technical partners, with close day‑to‑day clinical supervision. The goal is clinically relevant decision support with direct, measurable patient impact.

Inequalities in Climate Discourse

Political attention to climate change and disasters varies across countries and societal groups, and these differences may shape climate action. This PhD project uses large‑scale text analysis, NLP, and machine learning to quantify and explain variation in climate‑related attention across UN speeches and parliamentary debates. You will link discourse patterns to policy and action indicators, helping to clarify how representation and political salience influence climate action.

EXACT

In this PhD project, you will develop explainable and uncertainty‑aware graph‑based AI models to predict chemical toxicity for trustworthy environmental and regulatory decision‑making. Working at the interface of data science, graph theory, and computational toxicology, you will integrate large public toxicity datasets and translate model outputs into chemically interpretable insights. The project aims for methods that are both accurate and usable in practice.

TrustSeg

Medical image segmentation is central to radiotherapy and surgery, but clinical deployment requires not only accuracy—it requires knowing when a model is uncertain. This PhD project develops uncertainty‑aware segmentation methods that quantify and communicate confidence to support safer decision‑making in safety‑critical settings. You will work closely with clinicians and researchers to build trustworthy computer‑vision systems designed for real‑world clinical use.

All projects are described in detail (including your tasks, your profile and the application documents you need to submit) on our IDEAS website.

We offer

• Excellent supervision and optimal professional and interdisciplinary qualification through our HIGRADE graduate programme
• The freedom to master even the most demanding challenges between basic research and practical application
• The opportunity to work in interdisciplinary, international teams and benefit from a wide range of perspectives
• First class integration into national and international research networks to work together on global challenges
• Excellent research infrastructure and research data management to optimally support your work
• A wide range of options for balancing care responsibilities and work through our family office
• Competent support and advice for international colleagues arriving at the UFZ from the ‘International Office’
• A workplace in a vibrant region with a high life quality and social and cultural diversity

Your profile

Requirements vary by project, but what generally applies across the call:

• A very good Master’s degree (MSc or equivalent) in a relevant field (e.g., data science, computer science, mathematics/ statistics, physics, environmental sciences, life sciences/ bioinformatics, computational social science, or related areas), depending on the project.
• Strong programming/ data analysis skills and motivation to work with large, complex datasets and modern ML/AI methods.
• A strong interest in interdisciplinary research bridging data science and application domains, and the ability to collaborate in diverse teams across institutions and locations.
• Very good English skills (written and spoken) for work in an international research environment.

Please submit (1) a cover letter detailing your interest in the project(s) and a ranking for all project topics you would like to be considered for, (2) a transcript of records, and (3) two reference letters (letters themselves, not only names of referees) combined into one PDF file. More information on required documents is available on our website.

In your application, please rank all 8 projects according to your interest (1 = highest). Candidates will be ranked based on (i) scientific excellence, (ii) fit with IDEAS, and (iii) fit with the projects. Offers will be made based on the candidate ranking, while taking your project preferences into account to achieve the best match between candidates and projects.

Recruitment timeline

23 Feb – 06 Mar: Candidate ranking takes place

11 Mar – 20 Mar: Interviews

Please note that PIs from our project partners will also be involved in the selection process for this position, alongside UFZ employees.

The UFZ has a strong commitment to diversity and actively supports equal opportunities for all employees regardless of their origin, religion, ideology, disability, age or sexual identity.

We look forward to applications from people who are open‑minded and enjoy working in diverse teams.

Place of work

Leipzig or Dresden, depending on the project; mobile work possible

Working time

100% (39 h/week)

Contract limitations

limited contract / 3 years (extension by a fourth year is possible)

Remuneration according to the TVöD public‑sector up to pay grade 13 including attractive public‑sector social security benefits.

Your contact for any questions you may have about the job:

Please submit your application via our online portal with your cover letter, CV (please omit your photo, age, or marital status) and relevant attachments.