PhD Position in AI-Supported Transport Infrastructure Resilience – Zurich | Visa + Salary Insights

📍 Location: eu

🏷 Type: Full-time

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Job Overview

This PhD position at ETH Zürich focuses on advancing AI-driven methodologies for assessing the resilience of climate-neutral transport infrastructure. The role sits within the SHIFTIN EU Horizon project, which targets sustainable, biodiverse, and climate-resilient transport systems across Europe. The ideal candidate will work at the intersection of civil engineering, machine learning, and infrastructure systems modeling, contributing to surrogate models, stress-testing frameworks, and digital twin integration. This is a research-intensive role designed for candidates with strong quantitative skills and ambition to work on real-world infrastructure challenges at network scale.

📅 Job Timeline & Status

This role is in an early-to-mid recruitment cycle with a clearly defined deadline. Given ETH Zürich’s global reputation and EU Horizon funding, competition is expected to intensify closer to the deadline. Candidates should treat this as a high-urgency application opportunity and apply well before 31 July 2026.

🌍 Work Eligibility & Location

This is a fully ONSITE doctoral research position in Zurich. ETH Zürich maintains strong international recruitment policies, meaning non-EU candidates are typically eligible for visa sponsorship under PhD employment frameworks.

💰 Salary Intelligence

ETH Zürich PhD positions are generally well-funded compared to European averages. The estimated CHF 55k–65k range aligns with standard doctoral salaries in Switzerland, offering strong purchasing power and research stability.

📊 Role Breakdown

This PhD research focuses on developing advanced AI-driven tools for evaluating transport infrastructure resilience under climate stressors. A major component involves building surrogate models that approximate complex simulations using machine learning techniques such as neural networks, probabilistic models, and regression-based emulators. These models will support rapid stress-testing across thousands of hazard scenarios, including floods, heatwaves, and landslides. Approximately 40% of the work involves model development, while 25% focuses on integrating these models with network-level simulation frameworks and agent-based systems. Another 20% is dedicated to uncertainty quantification, interpretability, and validation against real infrastructure monitoring data. The remaining 15% includes collaboration with EU partners and contribution to digital twin platforms. Strong proficiency in Python, data science workflows, and optimization techniques is essential, as is the ability to translate research outputs into decision-support tools for real-world infrastructure planning.

🧩 Required Skills & Fit

📈 Difficulty & Competitiveness

This is a highly competitive doctoral position due to ETH Zürich’s global ranking and EU-funded project visibility. Applicants with strong ML + infrastructure systems backgrounds will have a significant advantage. Expect a rigorous selection process emphasizing research potential rather than only academic grades.

🚀 Career Impact

This PhD offers exceptional career acceleration into research leadership roles across AI for climate resilience, infrastructure analytics, and digital twin systems. Graduates often transition into top-tier academic positions or high-impact applied research roles in European innovation ecosystems.

📋 Key Responsibilities

The role involves designing and implementing AI-based surrogate models for large-scale transport infrastructure systems. You will develop computational frameworks for stress-testing infrastructure networks under climate-driven disruptions and integrate these models with digital twin platforms. A key responsibility includes analyzing interdependencies between transport modes such as rail, road, and ports using network science and simulation tools. You will also evaluate recovery strategies, maintenance optimization, and retrofit scenarios using data-driven decision-support systems. Collaboration is central, requiring coordination with EU partners, industry stakeholders, and academic researchers. The position also demands addressing uncertainty quantification, model explainability, and validation using real-world sensor and monitoring data from infrastructure systems.

🎯 Application Strategy

🧠 Application Optimization (Adaptive)

This section is personalized for high-impact research positioning.

🧠 Fit & Positioning Analysis

Evaluate alignment with ETH Zürich’s expectations before applying.

📅 Application Signals

The role is in an active recruitment phase with strong global visibility. As the deadline of 31 July 2026 approaches, application volume is expected to rise significantly. ETH Zürich typically operates on structured academic hiring cycles, meaning early submissions receive faster screening. Expect high competition from international engineering and AI candidates. Interview cycles may begin within weeks after review starts, making early application submission critical. Given the prestige and EU Horizon funding, this role should be treated as a high-urgency opportunity with potentially limited slots.

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✅ Job Source & Verification

This listing is sourced directly from ETH Zürich’s official job board, a highly credible academic employer and one of the world’s leading technical universities. Information reflects the official posting with last verification on 17 June 2026. Details such as salary are based on institutional norms due to the absence of explicit figures. Applicants should refer to ETH Zürich’s portal for the most current updates as postings may change without notice.