IDEAS PhD Researcher (f/m/x) Call 2027: 5 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 (e.g., machine learning, explainable AI, uncertainty quantification, and AI-ready FAIR data and research data management) to complex challenges in environmental and life sciences.
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 five PhD topics, of which four positions will be funded. Applicants can be considered for multiple projects and will be matched through a structured selection and ranking process.
FloodLens
Severe storms and floods cause large damages, and when occurring simultaneously over different regions, emergency responses and relief might be additionally strained. This project will combine advanced deep learning architectures and causal representation learning frameworks coupled with explainable AI to develop physically interpretable, robust, and trustworthy data-driven seasonal and sub-seasonal forecasts of spatially co-occurring flood events and their large-scale atmospheric precursors.
SoilCloudAI
Soil moisture can influence clouds, droughts, and heatwaves, but these feedbacks remain difficult to quantify because they are nonlinear, spatially connected, and strongly regime dependent. This project will develop interpretable and probabilistic graph-based AI methods to identify soil moisture–cloud feedback pathways from in-situ, satellite, reanalysis, and climate-model data. By combining Earth system science with modern data science, it aims to improve our understanding of land–atmosphere feedbacks and develop transferable methods for complex spatiotemporal environmental datasets.
TRACE-GBM
Can artificial intelligence design the next generation of radiotracers for brain tumors? Glioblastoma remains one of the deadliest human cancers and urgently requires improved tools for molecular imaging and targeted therapy. This project combines state-of-the-art generative protein design, machine learning, radiochemistry, and PET imaging to develop novel mini-protein binders against glioblastoma biomarkers. Through an iterative design-build-test-learn framework, computationally designed binders will be experimentally validated and translated into radiopharmaceutical probes for molecular imaging and theranostic applications. The project aims to establish a new paradigm for data-driven radiotheranostic development at the interface of AI, protein engineering, and neuro-oncology.
SafeBEEP
The loss of pollinating insects is caused by the utilization of plant protection productions with unintended side effects. Using data science and ai we intend to predict the elimination of plant protection products by the microbiome of pollinators, and so we could keep the bees safe. The prediction of transformation is already established for other microbiomes (take a look) and now we want to combine it with graph representation of chemical reactions.
DigitHealth
Continuous metabolic sensing technologies now enable high-frequency monitoring of metabolites such as lactate, generating rich physiological time-series data that capture tissue metabolism and adaptation beyond the capabilities of conventional sparse sampling. This PhD project aims to develop novel digital markers of tissue health from continuous metabolic sensing data by combining advanced biosensing technologies with machine learning and data science. The research follows a structured workflow:
Generate Data → Expand Biological Measurements → Learn Digital Markers → Predict Outcomes and Support Decisions.
Innovation Track
In addition to the advertised projects, IDEAS offers an Innovation Track for exceptional, self-developed project ideas. Your idea can be shaped freely, but must fall within Life Sciences & Health or Environmental Sciences. Before applying, you must obtain the support of two IDEAS PIs – one from a Helmholtz Center and one from a university (Y-supervision principle). Please visit our website for the full PI list and contact potential supervisors prior to submitting your application.
All projects are described in detail (including tasks, expected profile and the application documents you need to submit) on our IDEAS website
Place of work
Leipzig or Dresden, depending on the project; mobile work possibleWorking time
100% (39 h/week)Contract limitations
limited contract/ 3 years (extension by a fourth year is possible)
Salary
Remuneration according to the TVöD public-sector up to pay grade 13 including attractive public-sector social security benefits.Contact
Your contact for any questions you may have about the job:
Sandra Hille (UFZ | Tel. +49 341 6025 4674)
Anne Pidt (HZDR | Tel. +49 351 260 4716)
Your application
To ensure a fair selection process, please submit your application (cover letter, CV, and relevant supporting documents) via our online portal without a photo, age information, or details about your marital status.
Diversity and Inclusion
The UFZ values diversity and is actively committed to ensuring equal opportunities for all employees, regardless of their origin, religion, beliefs, disability, age or sexual identity.
We welcome people who represent diverse backgrounds, identities and perspectives. We therefore particularly encourage people who are affected by structural discrimination to apply to us.
The UFZ
The Helmholtz Centre for Environmental Research (UFZ) is a world-leading institution in environmental research and a member of the Helmholtz Association, Germany’s largest scientific organisation. With approximately 1,200 employees across Leipzig, Halle, and Magdeburg, we have been conducting research since 1991 using a transdisciplinary approach to address the most pressing challenges of our time: biodiversity loss, climate change, and environmental pollution. Our goal is to translate excellent research into practical solutions for policymakers, business, and society, and to serve as a reliable partner in supporting transformation processes toward a sustainable and just future for current and future generations. We foster a culture of collaboration, openness, and diversity within a work environment that actively promotes creativity and personal development.
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 (e.g., machine learning, explainable AI, uncertainty quantification, and AI-ready FAIR data and research data management) to complex challenges in environmental and life sciences.
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 five PhD topics, of which four positions will be funded. Applicants can be considered for multiple projects and will be matched through a structured selection and ranking process.
FloodLens
Severe storms and floods cause large damages, and when occurring simultaneously over different regions, emergency responses and relief might be additionally strained. This project will combine advanced deep learning architectures and causal representation learning frameworks coupled with explainable AI to develop physically interpretable, robust, and trustworthy data-driven seasonal and sub-seasonal forecasts of spatially co-occurring flood events and their large-scale atmospheric precursors.
SoilCloudAI
Soil moisture can influence clouds, droughts, and heatwaves, but these feedbacks remain difficult to quantify because they are nonlinear, spatially connected, and strongly regime dependent. This project will develop interpretable and probabilistic graph-based AI methods to identify soil moisture–cloud feedback pathways from in-situ, satellite, reanalysis, and climate-model data. By combining Earth system science with modern data science, it aims to improve our understanding of land–atmosphere feedbacks and develop transferable methods for complex spatiotemporal environmental datasets.
TRACE-GBM
Can artificial intelligence design the next generation of radiotracers for brain tumors? Glioblastoma remains one of the deadliest human cancers and urgently requires improved tools for molecular imaging and targeted therapy. This project combines state-of-the-art generative protein design, machine learning, radiochemistry, and PET imaging to develop novel mini-protein binders against glioblastoma biomarkers. Through an iterative design-build-test-learn framework, computationally designed binders will be experimentally validated and translated into radiopharmaceutical probes for molecular imaging and theranostic applications. The project aims to establish a new paradigm for data-driven radiotheranostic development at the interface of AI, protein engineering, and neuro-oncology.
SafeBEEP
The loss of pollinating insects is caused by the utilization of plant protection productions with unintended side effects. Using data science and ai we intend to predict the elimination of plant protection products by the microbiome of pollinators, and so we could keep the bees safe. The prediction of transformation is already established for other microbiomes (take a look) and now we want to combine it with graph representation of chemical reactions.
DigitHealth
Continuous metabolic sensing technologies now enable high-frequency monitoring of metabolites such as lactate, generating rich physiological time-series data that capture tissue metabolism and adaptation beyond the capabilities of conventional sparse sampling. This PhD project aims to develop novel digital markers of tissue health from continuous metabolic sensing data by combining advanced biosensing technologies with machine learning and data science. The research follows a structured workflow:
Generate Data → Expand Biological Measurements → Learn Digital Markers → Predict Outcomes and Support Decisions.
Innovation Track
In addition to the advertised projects, IDEAS offers an Innovation Track for exceptional, self-developed project ideas. Your idea can be shaped freely, but must fall within Life Sciences & Health or Environmental Sciences. Before applying, you must obtain the support of two IDEAS PIs – one from a Helmholtz Center and one from a university (Y-supervision principle). Please visit our website for the full PI list and contact potential supervisors prior to submitting your application.
All projects are described in detail (including tasks, expected profile and the application documents you need to submit) on our IDEAS website
Your tasks
Your tasks will depend on the project you are matched to. Across all projects, you will conduct original PhD research at the interface of data science and domain science, contribute to publications and scientific dissemination, and participate in IDEAS training and cohort activities (e.g. seminars, coursework, and community events).
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
- Firstclass 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’
- Special annual payment, capital-forming benefits and subsidised Deutschland-Job-Ticket
- 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.
Application documents:
Please use the application builder on our website to provide your project preferences and additional project-relevant information, and to combine the following documents into one PDF file: (1) a cover letter, (2) your Master's certificate, (3) a transcript of records, and (4) two reference letters (if available). Upload the combined document here in our recruiting system.
Please note that incomplete applications cannot be considered.
Candidates will be ranked based on (i) scientific excellence, (ii) fit with IDEAS, and (iii) fit with the projects. Offers are made according to this ranking, taking your project preferences into account to achieve the best candidate–project match.
Recruitment timeline
10–21 Aug: Candidate ranking
24 Aug–4 Sept: Interviews
Please note that PIs from our project partners will be involved in the selection process alongside UFZ and HZDR staff.
Application deadline: 26.07.2026