Are you passionate about climate change adaptation, plant genomics, and AI-driven research? A fully funded PhD opportunity in Genomic Vulnerability and Climate Resilience is now open at the prestigious Université Paris-Saclay in France. This interdisciplinary PhD combines environmental genomics, machine learning, statistics, and plant science to build tools for understanding species resilience under climate stress.

About the PhD Program

As climate change accelerates, predicting how plant populations respond to extreme environmental shifts is critical for ensuring global food security. This PhD project aims to advance our understanding of genomic adaptation in crops like maize and teosinte under future climate scenarios.

Key Objective: Develop cutting-edge statistical and deep learning methods to model genomic vulnerability using Genome-Environment Association (GEA) and Genomic Offset (GO) frameworks.

Why Choose This PhD?

• Host Laboratory: MIA Lab at INRAE, Université Paris-Saclay — a hub for innovation in statistical modeling and quantitative genetics.
• Expert Supervision: Work under the guidance of leading scientists including Julie Aubert, Tristan Mary-Huard, M. Tenaillon, Laurence Moreau, and Mathieu Gautier.
• Collaborative Research: Jointly conducted with UMR Génétique Quantitative et Évolution and the University of Montpellier.
• Real-World Relevance: Focused on analyzing genomic data of maize and teosinte to assess their drought tolerance and climate resilience.

Research Highlights

• Climate change impact modeling
• Environmental and adaptive genomics
• Machine learning & neural networks
• High-dimensional data analysis
• Variational inference & stochastic optimization

Who Should Apply?

If you have a strong background in applied mathematics, machine learning, or statistics, and you’re curious about genomics and environmental adaptation, this opportunity is tailor-made for you.

Eligibility Criteria:

• Master’s degree (or equivalent) in Applied Mathematics, Statistics, Bioinformatics, Machine Learning, or related fields
• Proficiency in Python and/or R
• Interest in plant biology, climate science, and computational genomics
• Motivation to work on real-world problems with an interdisciplinary team

Program Details

• Degree Level: PhD in Environmental and Computational Genomics
• Location: Université Paris-Saclay, France
• Start Date: September or October 2025
• Duration: 3 years
• Funding Source: CLand Project & PEPR Maths-Vives
• Doctoral School: École Doctorale de Mathématiques Hadamard (EDMH)

How to Apply

Interested candidates should submit the following documents via email:

• CV (with academic background and skills)
• Cover Letter (outlining your motivation and fit for the project)
• Contact details of two academic referees

Send Applications To:

• Tristan Mary-Huard: tristan.mary-huard@agroparistech.fr
• Julie Aubert: julie.aubert@agroparistech.fr

Deadline: Applications are reviewed on a rolling basis. Early submission is highly encouraged!

Join a Leading Interdisciplinary Lab in AI, Remote Sensing & Climate Research

Led by Dr. Qian Liu at the School of Natural Resources (SNR), this newly established research group focuses on using AI-powered geospatial analytics to address pressing challenges in environmental monitoring and climate resilience.

This is your chance to work at the intersection of:

• Artificial Intelligence (AI)
• Remote Sensing
• Geospatial Data Science
• Climate and Atmospheric Research

Research Focus Areas

Students in the lab will engage in impactful, data-driven research projects. Core themes include:

• Remote sensing and atmospheric monitoring
• AI-based analytics for climate systems and weather forecasting
• Big data processing for environmental applications
• Spatiotemporal modeling using Earth observation data
• Extreme weather event detection and climate risk mitigation

Who Should Apply?

Ideal candidates should have:

• A background in atmospheric science, remote sensing, computer science, geospatial science, or a related discipline
• Proficiency in programming, data analysis, and/or machine learning
• A strong interest in interdisciplinary research with real-world environmental applications

Program Highlights

Why pursue your PhD or Research Assistantship at the University of Missouri?

• Join a collaborative, fast-growing lab with access to advanced geospatial and AI tools
• Work on real-world environmental challenges with global impact
• Collaborate with experts in machine learning, climate science, and geospatial modeling
• Develop solutions for climate adaptation, natural disaster resilience, and sustainability

Location & Start Date

• Location: University of Missouri, Columbia, Missouri, USA
• Start Date: Fall 2024

How to Apply

To be considered for these positions, please email the following documents to Dr. Qian Liu at qian.liu@missouri.edu:

1. Curriculum Vitae (CV)
2. A brief research statement outlining your interests and goals
3. Contact information of academic references

Don’t Miss This Opportunity

This is a unique opportunity to join a forward-thinking research group at a top U.S. institution. If you’re ready to make a difference in climate and environmental science using AI and remote sensing, apply today!

PhD Research Topic:

Impact of Climate on Radionuclide Transfer to Plants

With rising global temperatures, it’s essential to understand how radioactive and chemical pollutants behave in terrestrial ecosystems. This PhD project investigates how climate change affects radionuclide transfer from soil to plants, focusing on two contrasting climatic zones:

• Temperate/humid climates (e.g., Europe)
• Hot/dry climates (e.g., UAE)

A unique aspect of this research is the inclusion of soil microbial communities in radionuclide uptake analysis—providing a comprehensive understanding of pollutant dynamics under climate stress.

Key Research Objectives

• Analyze radionuclide transfer mechanisms between soil and plants under different climate scenarios
• Investigate the influence of rhizospheric microbial communities on pollutant uptake
• Develop predictive environmental models to forecast pollutant behavior due to climate change

Methodology & Experimental Setup

The PhD research will employ state-of-the-art methods, including:

• Nucleated phytotron experiments for climate simulation (Subatech lab)
• Hydroponic systems to study radionuclide bioavailability
• Soil/sand pot trials for real-world transfer simulations across diverse soil and climate types
• Use of radionuclide analogues in non-nuclearized phytotrons (LPG lab)

Candidate Profile: Eligibility & Skills

Essential Qualifications:

• Master’s degree in Earth Sciences, Environmental Sciences, or related fields
• Solid understanding of life and earth sciences
• Hands-on experience with phytotron-based plant growth systems
• Knowledge of pollutant tracing techniques and research valorization
• Strong communication skills in English (spoken and written)
• Demonstrated organizational skills, autonomy, and scientific rigor

Preferred Expertise (Bonus Skills):

• Background in radiochemistry and/or microbiology
• Familiarity with radioecology, soil science, or plant-microbe interactions

Research Supervision Team

You’ll work under the guidance of an experienced academic team:

• Prof. A. Abdelouas – IMT Atlantique
• Prof. T. Lebeau – Nantes Université
• Dr. O. Péron – Nantes Université

Support and training will be provided by both Subatech and LPG laboratories.

Application Deadline:

May 9, 2025 – 23:59 (UTC)

Application Checklist

Prepare the following documents for your application:

• Updated CV
• Motivational cover letter detailing your research interests and relevant experience

How to Apply

Apply directly through the CNRS application portal:

Click Here to Apply Online

Research Focus:

Optimization and Advanced Control of Sequential Batch Operation in High Rate Algal Ponds (SBR-HRAPs)

Why This PhD Matters

Traditional wastewater treatment methods, such as the activated sludge process, consume high energy and offer limited nutrient recovery. In contrast, algae-based bioprocesses like HRAPs provide an eco-friendly, low-cost alternative—especially suitable for rural, decentralized, and arid regions.

Despite the advantages, integrating sequencing batch reactor (SBR) operation into HRAPs remains underdeveloped. This PhD project aims to design, model, and implement intelligent control systems to enhance the performance of SBR-HRAPs under diverse environmental conditions.

PhD Objectives

• Predict and optimize treatment efficiency in varying environmental conditions
• Improve operational control: aeration, mixing, and feed intervals
• Reduce energy consumption while enhancing nutrient recovery
• Ensure compliance with water reuse and public health standards
• Build a real-time digital twin for process monitoring and optimization

Research Methodology

This is an interdisciplinary PhD blending lab experiments, process modeling, and real-world pilot testing:

Phase 1 – France (GEPEA Laboratory, Nantes Université):

• Develop and simulate control algorithms using the AlgoSolis research platform

Phase 2 – Australia (Flinders University):

• Validate models in a live HRAP pilot facility at SA Water’s Angaston site
• Compare performance of CSTR-HRAP vs SBR-HRAP configurations

Supervision & Mentorship

Nantes Université:

• Prof. Jérémy Pruvost (Bioprocess Engineering)
• Dr. Mariana Titica (Bioprocess Engineering)
• Mr. Guillaume Cogne (Bioprocess Engineering)

Flinders University:

• Prof. Howard Fallowfield (Applied Phycology, Public Health)

Program Highlights

• Dual PhD degree from Nantes Université and Flinders University
• No tuition fees at one institution
• Research stays split across France and Australia (18–24 months each)
• Fully funded (stipend + research expenses)
• Access to state-of-the-art labs and field sites
• Develop career-ready expertise in clean water technologies, algal bioprocessing, and digital control systems

Preferred Candidate Background

Applicants should have demonstrated expertise or strong interest in:

• Bioprocess engineering
• Process modeling and simulation
• Control systems design
• Algal biotechnology and photobioreactors
• Sustainable or solar-driven wastewater technologies

Apply Now: Submit your application online

Contact & More Info

For further information or queries, please reach out to the program coordinators listed on the application portal.

Shape the Future of Wastewater Treatment

This PhD opportunity is perfect for aspiring researchers who want to contribute to:

• Eco-innovative water treatment solutions
• Circular economy and resource recovery
• Climate-resilient and low-energy biotechnologies

Earn a globally recognized dual PhD while driving the next generation of algae-based wastewater innovation.

Location:

Position Type:

Start Date:

Are you passionate about remote sensing, snow and ice dynamics, and Earth observation technologies? ETH Zurich is offering a fully funded PhD opportunity in the field of cryosphere research using Synthetic Aperture Radar (SAR). This prestigious doctoral position is hosted by the Institute of Environmental Engineering (IFU) at one of the world’s top technical universities.

About the Project: Remote Sensing of Snow & Ice with SAR

This PhD project focuses on using multi-modal high-frequency SAR data to analyze and monitor snow and glacier behavior in the cryosphere. The research is structured around two primary themes:

1⃣ Glacier Elevation Change & Mass Balance Estimation

• Generate time series of Digital Elevation Models (DEMs) using SAR interferometry
• Quantify large-scale glacier and ice sheet volume changes
• Estimate glacier mass balance using spatial-temporal datasets
• Apply validated methods to new geographic locations and time frames

2⃣ Glacier Crevasse Detection & Ice Dynamics

• Develop cutting-edge techniques for crevasse identification using high-resolution SAR
• Explore SAR modes including polarimetry, interferometry, and tomography
• Integrate airborne and satellite SAR data for advanced cryosphere research

This interdisciplinary project includes access to:

• International snow and glacier test sites
• Ground validation datasets
• Collaborations with global experts in remote sensing and climate science

Eligibility & Candidate Requirements

ETH Zurich is looking for an enthusiastic and skilled researcher with the following qualifications:

• A Master’s degree (or equivalent) in Electrical Engineering, Geomatics, Geophysics, Computational Science, Physics, or a related technical field
• Strong programming skills in Python, C/C++, or similar languages
• Experience in remote sensing, SAR, large-scale data processing, or snow/ice modeling is advantageous
• Excellent English communication skills (both written and spoken)
• Possession of a valid European Category B driving license

Why Join ETH Zurich?

ETH Zurich offers a vibrant and inclusive research environment with:

• Access to world-class facilities and expert mentorship
• A collaborative, international team culture
• Equal opportunity policies promoting diversity and innovation
• Strong support for doctoral researchers in academic and career development

How to Apply

To apply for this exciting PhD opportunity in remote sensing at ETH Zurich, submit your complete application online via the official ETH Zurich career portal. Email applications will not be accepted.

Your application should include:

• A cover letter detailing your motivation and relevant experience
• Your updated CV
• Copies of academic transcripts (including ongoing coursework)
• A PDF version of your Master’s thesis
• Contact details of two academic references

Apply Online Now

For further inquiries (not applications), you may contact Prof. Irena Hajnsek at:
ihajnsek@ethz.ch

About ETH Zurich

ETH Zurich is globally recognized as a leader in science, engineering, and innovation. With over 30,000 students and staff from more than 120 countries, ETH Zurich is dedicated to solving real-world challenges through cutting-edge research and interdisciplinary collaboration. From its base in Zurich, Switzerland, the university contributes to sustainable global development with local impact.