PhD Student in Trustworthy AI for Autonomous Plant Operations

Summary: Develop a trustworthy AI framework for autonomous plant operations, focusing on AI agents that monitor plant state, detect anomalies, and recommend actions while ensuring safety and reliability in safety-critical environments.

The Intelligent Maintenance and Operations Systems (IMOS) Lab at EPFL is looking for a motivated and out-of-the-box thinking PhD researcher, (100%, in Lausanne, fixed-term) starting in November or upon agreement.

PROJECT DESCRIPTION

The objective of this project is to develop a trustworthy AI framework for the autonomous operation of process plants. The research will focus on AI agents that monitor the plant state, detect and diagnose abnormal situations, and recommend or execute operating actions, while meeting the safety, reliability and transparency requirements of safety-critical environments.

The project will explore how learning-based and agentic AI systems (e.g., agents built on large language or foundation models) can be combined with physics-informed process models, digital twins and engineering knowledge such as P&IDs and operating procedures. A particular emphasis lies on trustworthiness: quantifying uncertainty, verifying agent decisions before they reach the plant (e.g., doer–checker architectures), explaining recommendations to operators, and remaining robust to sensor faults, distribution shifts and previously unseen operating conditions.

Applications will include complex process plants, where autonomous decisions directly affect safety, product quality and asset lifetime.

WORK ENVIRONMENT

EPFL is one of the most dynamic university campuses in Europe, ranks among the top 20 universities worldwide and offers an exceptional working environment with very competitive salaries. The IMOS Lab (https://www.epfl.ch/labs/imos/ ) offers a highly motivating, interdisciplinary scientific environment with many opportunities to interact across projects and researchers, and maintains an excellent network of collaborations with industrial stakeholders and leading international universities.

CANDIDATE PROFILE

We are looking for a PhD candidate with a strong analytical background and an outstanding MSc degree in Computer Science, Robotics, Chemical/Process, Mechanical or Electrical Engineering, Control, Applied Mathematics, or a related field. You should have a solid foundation in machine learning and strong programming skills (Python, deep learning frameworks such as PyTorch), ideally with experience in one or more of the following: large language models and agentic AI, uncertainty quantification, anomaly detection and fault diagnosis, or decision-making under uncertainty.

Prior experience with process systems engineering or process control, physics-informed or hybrid modeling, explainable AI, or the verification and safety assurance of learning-based systems is considered a strong asset, as is experience with industrial time-series data or process simulators.

We expect the candidate to be self-driven, with strong problem-solving abilities and out-of-the-box thinking. Professional command of English (both written and spoken) is mandatory.

APPLICATION PROCESS

Formal applications including:

• a letter of motivation,
• a CV of the candidate,
• brief research statement (one page) describing your project idea in the field of trustworthy AI for autonomous plant operations, making connections to your experience and related work from the literature,
• transcripts of all obtained degrees (in English),
• one publication (e.g. thesis or preferably a conference or journal publication, a link is sufficient),

Should be submitted via the application platform.

Further information on EPFL IMOS Lab can be found under: https://www.epfl.ch/labs/imos/

Shortlisted candidates will be invited to apply to one of the [EPFL doctoral schools](https://www.epfl.ch/education/phd/programs/) (e.g. EDRS, EDCE or [EDEE](https://www.epfl.ch/education/phd/programs/edce-civil-and-environmental-engineering-2/)). This parallel application process is necessary to be eligible for a PhD at EPFL. Please check [this page](https://www.epfl.ch/education/admission/admission-2/phd-admission-criteria-and-application/) for additional information on admission.

Application deadline: 31.10.2026

Contract Start Date: 01.11.2026

Activity Rate Min: 100

Activity Rate Max: 100

Contract Type: CDD

Duration: 1 year renewable

Reference: 2494

For more information, please contact: thy.maire@epfl.ch