CFD expertise required by fire engineering company. 
More details can be provided on request. 
Hybrid working, home working, office.
Salary up to 150K per annum depending on experience.

We are looking for a highly motivated, skilled, and persistent PhD student with 
experience in computational fluid dynamics (CFD) and some knowledge in structural 
analysis. The research aims to investigate flow-induced forces in hydraulic 
turbines under varying operational conditions and how these forces affect the 
degradation and lifetime of the machines.

More information and application form:
https://www.chalmers.se/en/about-chalmers/work-with-us/vacancies/
(search for "PhD Student: CFD for Hydropower Lifetime Analysis")
Direct link:
https://www.chalmers.se/en/about-chalmers/work-with-us/vacancies/?
rmpage=job&rmjob=13949

Applications through e-mail are not accepted, so please don't send them. But get 
in touch with questions.

TITLE: PhD on Multiphase flows for environmental applications

The Department of Mechanical Engineering of the University Rovira i Virgili of Tarragona is looking for an outstanding and committed candidate for PhD studies in the field of computational and experimental fluid dynamics.

PROJECT TITLE: Numerical and experimental study of liquid-gas multiphase flows with particles

CALL LINK: https://seuelectronica.urv.cat/treballar-a-la-urv/2025pmfpipfenglish.htm

CONDITIONS OF THE COMPETITION: https://seuelectronica.urv.cat/media/upload//arxius/PI/Bases_2025PMF-PIPF_ENG.pdf

SKILLS REQUIRED: Bachelor and Master in Engineering, Physics or Mathematics

STUDENTSHIP: The project is planned for 4 years, with a monthly gross salary of 1,515,03 euros during the first and second year, 1,623,25 euros during the third year and 2.029,06 euros during the fourth year. Also cost of tuition is covered.

APPLICATION PROCESS: To apply for this position, you should access to the following link (register is required) and follow the instructions.
https://appsrecerca.urv.cat/cgi-bin/programes/application/inici.cgi?conv=2025PMF-PIPF-&idioma=ENG
E-mail applications are not accepted.

DEADLINE
June 13th, 23:59 (Spanish time)

Please don't forget to apply for our project (reference 2025PMF-PIPF-16).

This position is funded by NASA. The position requires regular progress reports 
to the agency. The postdoc researcher will perform research in computational 
aerodynamics and flow control; thus, an established background in these areas is 
a must. Knowledge of aircraft design and machine learning is a plus. This 
position requires active collaboration with the propulsion and structure teams 
to identify and implement modifications to a baseline aircraft design to improve 
the aerodynamic performance by leveraging the expanded design space provided by 
electric propulsion. The position requires documenting and disseminating the 
team's findings through peer-reviewed scientific articles. Reliable and 
consistent attendance is crucial, as is maintaining collegial and effective 
working relationships with other team members within and outside the University 
of South Florida.

Minimum Qualifications:

PhD in Mechanical or Aerospace Engineering
Experience in computational aerodynamics and flow control 
Experience in CFD and machine learning
Preferred Qualifications:

Experience with using optimization tools for design
Experience with aircraft design
Additional Information:

This position is renewable annually based on continued need for the position, 
availability of funding, and satisfactory job performance.

****This job is subject to Foreign Influence Screening: During the 2021 Florida 
legislative session, House Bill 7017 (“HB 7017”) was passed and later signed 
into law by Governor DeSantis.  The new law modifies a variety of Florida 
Statutes to address concerns regarding improper foreign influence.  With regard 
to employment at major research institutions like the University of South 
Florida, the new law requires a thorough review of certain applicants for 
positions involving research.  

Cavitation is a complex phenomenon that occurs in fluid dynamics, characterized 
by the formation, growth, and implosive collapse of vapor-filled cavities in 
regions of low pressure. A major effect of cavitation is noise which propagates 
in the flow field and also in air. This research project aims to develop a 
comprehensive model to understand the dynamics of cavitation, investigate its 
effects on underwater radiated noise, and explore engineering applications. 
Using advanced computational fluid dynamics (CFD) techniques, the research 
develops a novel mathematical model able to reproduce cavitation dynamics, 
including shock wave propagations and bubble implosion using a compressible 
mixture model available in the software Openfoam. Also, the project aims at 
development of an acoustic model able to reproduce the cavitation induced noise, 
in particular that associated to bubble implosion. The model will be validated 
considering literature test cases and successively used for the analysis of 
noise generated by cavitation in two main industrial applications, namely a ship 
propeller and within an hydraulic pump.
The ideal candidate has a background in applied physics, mathematics or 
engineering and knowledge of basic fluid mechanics. He/she is highly motivated 
in pursuing a PhD project in which it is required development of novel 
mathematical models for multiphase flows and massive use of computational fluid 
dynamics.Info on the application procedure are below:
https://phd.units.it/en/dot1333212/how-apply

The division of Numerical Fluid Dynamics at TU Berlin has an open PhD 
position to conduct numerical investigations into the fundamental properties of 
wall-bounded turbulent flows. 

Tasks
- Contribution to teaching, including the delivery of tutorials and practical 
classes and the supervision final theses
- Research in the field of numerical fluid dynamics, with a particular focus on 
the investigation of turbulent boundary layers
- Conducting numerical simulations and experimental work, developing simulation 
programs
- Preparation of applications for third-party funding and scientific 
publications

Requirements
- Successfully completed scientific university degree (Master, Diplom or 
equivalent) preferably in Engineering Science, Mechanical Engineering, Aerospace 
Engineering or Scientific Computing
- Very good knowledge of one or several of the following areas: applied 
mathematics, fluid mechanics or numerical methods
- Proficiency in programming
- The ability to teach in German and/or in English is required; willingness to 
acquire the missing language skills.

Desirable (optional):
- Experience in programming in Fortran or C/C++
- Basic knowledge of parallel computing

Full details can be found under
https://www.jobs.tu-berlin.de/en/job-postings/193236

Please send your application with the reference number (V-148/25) and the usual 
documents - bundled in one PDF document - by email to an Prof. Dr. Busse via 
office@tnt.tu-berlin.de.

This research focuses on advancing state-of-the-art aerodynamic design 
methodologies to significantly enhance wind energy harvesting in urban settings. 
The primary objective is to develop a high-fidelity CFD–machine learning (CFD-ML) 
framework capable of efficiently analyzing and optimizing the coupled 
interactions among urban wind dynamics, rooftop flow structures, and vertical-
axis wind turbines. With a focus on building-integrated wind energy systems, this 
project aims to push the boundaries of current technology by identifying optimal 
aerodynamic configurations that maximize wind capture efficiency and mitigate 
turbulence under diverse urban layouts and meteorological conditions. To achieve 
this, the project explores machine learning approaches—including surrogate 
modeling, and reinforcement learning—to accelerate CFD optimization and enable 
adaptive control strategies for complex urban wind environments. The ultimate 
goal is to deliver a scalable, high-performance solution that supports continuous 
and efficient decentralized power generation in densely populated areas.

More information about this position and the application procedure can be found 
here:https://www.linkedin.com/feed/update/urn:li:activity:7325783970170736641/

This is an exciting opportunity to join a team to develop state of the art high order flux reconstruction solver 
for turbomachinery flows. The focus of the role is to develop a well validated in-house FR solver.

What we offer:
1. A competitive salary, around 400,000 RMB per year.
2. A holiday entitlement up to 28 days per year.
3. Accommodation near the campus.

This position is funded for maximum three years. For more information, please contact Professor Jiahuan Cui 
directly at jiahuancui@intl.zju.edu.cn

The University of Exeter and Oxford Instruments Plasma Technologies are offering 
a jointly funded PhD position in computational and machine learning modelling of 
low temperature plasmas. Oxford Instruments (OI) develops and markets a range of 
manufacturing and scientific equipment using low temperature plasmas for etching 
and deposition. Plasma is a complex state of matter which can be considered as a 
fluid or as individual particles; moreover, complex chemical reactions can occur 
between species in the plasma. Modelling a plasma is accordingly a very complex 
and challenging task. The objective of the project is to optimise the hardware 
for the control of plasma in an atomic layer deposition chamber using various 
computational modelling approaches. This will require a hybrid fluid/particle 
model, which will be developed using the OpenFOAM toolkit  A modelling workflow 
will be created, and then used as the basis for the optimisation, potentially 
using tools such as Bayesian Optimisation. In addition, novel machine learning 
approaches will be investigated as a faster alternative for modelling the 
injector.

The technological impact of this work will be quite significant. Plasma etching 
is an important stage in manufacturing of microprocessors and other electronic 
devices, and any advance in this manufacturing is likely to have significant 
benefits. Computational modelling such as is investigated here can be the key to 
more efficient manufacturing and enable OI to push the envelope of what is 
possible. The modelling being developed here also have significant applications 
in other areas of plasma research.

The studentship will be awarded on the basis of merit. The project will involve 
computational modelling using physics- and machine learning-based methods and 
would suit a top student with a background in Physics, Engineering, Mathematics 
or similar disciplines with an interest in computer modelling. Students who pay 
international tuition fees are eligible to apply, but should note that the award 
will only provide payment for part of the international tuition fee (~£24k) and 
no stipend.  International applicants need to be aware that they will have to 
cover the cost of their student visa, healthcare surcharge and other costs of 
moving to the UK to do a PhD.

The conditions for eligibility of home fees status are complex and you will need 
to seek advice if you have moved to or from the UK (or Republic of Ireland) 
within the past 3 years or have applied for settled status under the EU 
Settlement Scheme.  

The collaboration involves a project partner who is providing funding [and other 
material support to the project], this means there are special terms that apply 
to the project, these will be discussed with Candidates at Interview and fully 
set out in the offer letter.  The collaboration with the named project partner 
is subject to contract.  Please note full details of the project partner’s 
contribution and involvement with the project is still to be confirmed and may 
change during the course of contract negotiations.  Full details will be 
confirmed at offer stage.

Full details and application here: 
https://www.exeter.ac.uk/study/funding/award/?id=5497