洛桑联邦理工学院、乌得勒支大学最新岗位制博士项目分享

本期为大家推荐洛桑联邦理工学院、乌得勒支大学最新2025岗位制博士项目信息。

洛桑联邦理工学院

PhD position - Experimental study of vortex interactions with application to hydropower

EPFL |Unsteady Flow Diagnostics Laboratory (UNFoLD)

APPLICATION DEADLINE: 招满即止

Mission

TheUnsteady Flow Diagnostics Laboratory (UNFoLD)led by Prof. Karen Mulleners at EPFL in Lausanne is looking for a new PhD student to experimentally study the formation and interaction of vortices with application to hydropower in the framework of the collaborative project VORTEX-FLEX with the group of Dr. Elena Vagnoni at theTechnology Platform for Hydraulic Machines (PTMH)at EPFL funded by the Swiss Federal Office of Energy (SFOE).

Our lab specialises in the experimental measurements, analysis, and modelling ofunsteady vortex-dominated flow phenomena, with past applications in bio-inspired propulsion, wind turbine rotor blade aerodynamics, and gust interactions. Our research focuses on unfolding the origin and development of unsteady flow separation and vortex formation and we have built a reputation for carefully designed and precisely controlledexperiments, high qualitytemporally and spatially resolvedfield experiments using particle image velocimetry combined with synchronised measurements of the technically relevant quantities, such as aerodynamic forces or power output,exploratory data analysesto bridge the gap between our observations and understanding of the development and interaction of vortices, and the technically relevant quantities.

Switzerland has a strategic goal to increase hydropower electricity production to 37,400 GWh by 2035 and to 38,600 GWh by 2050, which highlights the critical role of hydropower in securing the national energy supply. To meet this goal, there is an urgent need for a thorough review of technical setups and operational strategies, including more flexible operations and more frequent start-up cycles to guarantee grid stability. The transient flow behaviour during start-up cycles leads to the formation of vortical structures that have a different topology than the vortices that develop during steady-state operation and lead to adverse fluid-structure interactions increase e.g. fatigue damage. The collaborative VORTEX-FLEX project aims to gain insight into the parameters that govern the formation, interaction, and evolution of the vortices during the start-up process todevelop guidelines for optimal start-up procedures and accurately predict the expected lifetime and maintenance intervals of hydraulic machines. Your role in this project will be todevelop a new experimental setup and systematically analyse and model the topological and temporal characteristics of draft-tube-like vortical structures to complement theexperiments in a fully homologous reduced-scale model of the hydraulic machine that will be conducted by our partners at PTMH.

Main duties and responsibilities

• Perform original research in the field of experimental unsteady vortex dynamics and hydropower.
• Lead and contribute to publications in scientificjournals.
• Contribute to general lab activities (including teaching assistance and co-supervision of student projects)

Profile

• A master'sdegree in engineering, environmental sciences, or physics.
• Strong interest in experimental fluid mechanics and hydropower.
• Experience withexperimental (fluid)mechanicsor in designing laboratory experiments.
• Excellent written and oral communication skills in English. (French is not required.)

We offer

• Opportunity to perform state-of-the-art research in one of the most dynamic scientific institutions in Europe.
• Competitive salary and excellent educational conditions.
• Term of employment: 1-year fixed-term contract (CDD), renewable for 4 years.

Informations

Interested applicants should upload the following documents:

• CV
• Motivation statement
• Gradesfrom bachelorand master studies

If you have any questions, please feel free to contact Prof. Karen Mulleners (karen.mulleners@epfl.ch).

乌得勒支大学

PhD Position in Learning Graphical Models for Risk-Based Inspection

Utrecht University| Department of Information and Computing Sciences

APPLICATION DEADLINE: 26 Mar 2025

Your job

The Dutch government inspectorates play a critical role in safeguarding public interests such as food safety, a clean environment, and quality of education. To ensure effective oversight with a limited capacity at strategic and operational level, inspectorates need to work in a data-driven way and embed AI technology in their primary processes.

By joining the ICAI lab AI4Oversight, you join a community that collaborates to address AI challenges specific to the inspection domain leading to scientifically attested methods. The AI4Oversight lab connects the Human Environment and Transport Inspectorate (ILT), the Netherlands Labour Authority (NLA), the Inspectorate of Education (IvhO), Netherlands Food and Consumer Product Safety Authority (NVWA), Netherlands Organisation for Applied Scientific Research (TNO), Utrecht University and Leiden University. Collaboration between these organisations is seen as an essential element of our lab. Working together enables not only to develop new knowledge, but also to use each other’s expertise, to experiment together, to learn from each other and to bring theory to practice.

The execution of the research will be highly participatory. You will spend time at the offices of funding partners and have the opportunity to dive into the practical challenges and way of working of the partners. You will work together with data scientists of the inspectorates, who will contribute with practical experiences and use cases. Within the AI4Oversight Lab you will be part of a collaborative environment with at least five other PhD candidates, where you regularly engage in knowledge exchanges to strengthen cross-disciplinary collaboration.

Your work aims to advance risk classification beyond binary labels by learning interdependencies between inspection items using probabilistic graphical models like Bayesian networks. These models aim to support interactive inspections by prioritizing items dynamically, combining data-driven learning with expert knowledge to handle incomplete information effectively.

Your key responsibilities will be to:

• conduct original research in the field of learning graphical models from data for the purpose of risk-based inspection;
• publish and present scientific articles at international journals and conferences;
• collaborate with other PhD candidates in the AI4 Oversight lab, researchers at the partners’ data science labs, and the intended users and other stakeholders;
• contribute to the teaching tasks of the department (10-15% of your time).

Your qualities

You are equipped with a critical mindset and motivated to use your experience in education and research to make a valuable contribution to research in the field of artificial intelligence and machine learning. Next to that you have the following qualifications:

• a MSc degree in Artificial Intelligence, Data Science, Computer Science, Mathematics or a related field;
• demonstrable programming skills, preferably in Python;
• solid experience with machine learning, in particular with graphical models such as Bayesian networks;
• the ability to work with diverse stakeholders, such asindustry professionals andacademic researchers;
• proficiency in English (spoken and written);
• proficiency in Dutch is considered an advantage, as you will be working in close cooperation with Dutch organisations.

A background check may be part of the selection procedure.

Our offer

• A position for four years;
• a gross monthly salary between €2.901and €3.707in the case of full-time employment (salary scale P under the Collective Labour Agreement for Dutch Universities (CAO NU));
• 8% holiday pay and 8.3% year-end bonus;
• a pension scheme, partially paid parental leave and flexible terms of employment based on the CAO NU.

Apply now

If you are enthusiastic about this position, just apply via the 'apply now' button. Please enclose:

• your motivation letter, in which you address the skills and knowledge you have gained during your Master's studies that will contribute to your success in this PhD programme;
• your curriculum vitae, in which you also state the names, affiliations, telephone numbers, and email addresses of at least two referees who have agreed to be contacted.
• a copy of your Master's diploma and grade lists;
• a copy of your Master's thesis.