Applications are invited for a fully funded PhD studentship working on 
machine learning to address some of today’s engineering challenges. The PhD 
student will join an international 
research collaboration between world-leading research teams based at the 
University of Southampton, which is a member of the Russell Group and ranked 
in the world’s top 100 
Universities, and ZHAW, one of the leading universities of applied sciences 
in Switzerland. Among other international awards, the Southampton team was 
honoured to receive the 2018 Best 
Technical Paper Award (https://aerospaceamerica.aiaa.org/bulletin/november-
2019-aiaa-bulletin/) from American Institute of Aeronautics and Astronautics 
(AIAA).

Numerical modelling of complex physical systems has become one of the most 
important steps in the efficient design and analysis of aerospace systems. 
However, due to the complexity of 
the physics and the computational modelling of multi-scale dynamical 
systems, computational costs may be prohibitive. Consequently, predictive 
models and control schemes that cannot 
account for or take advantage of efficient algorithms have very limited 
applicability. The crucial question posed in this PhD project is: “Can we 
develop a sparsely-interconnected 
reduced order model, combining data-driven learning with a physics-based 
nonlinear reduced order modelling technique?”
The PhD project builds on the methodology developed by Dr Da Ronch for 
coupled, non-linear systems. The resulting nonlinear reduced order model 
contains a quadratic tensor, with size 
growing as the cube of the selected modes. The overarching idea is to 
develop a framework, which is both model- and data-driven, to extract a 
compact, reduced representation of the 
reduced order model. Sparsity features of the model are maximised by 
appropriate machine learning algorithms that identify the relevant 
interactions. The minimization of the 
sensitivity to aleatory uncertainties is part of the requirements for the 
reduced order models. The work will focus on systems involving fluid 
mechanics and fluid-structure interaction 
problems.

The 3.5-years studentship covers UK/EU level tuition fees. It is planned to 
start the project in 2021, preferably no later than 31 July. The funding 
available is competitive and will 
only be awarded to an outstanding applicant. As part of the selection 
process, the strength of the whole application is considered, including 
academic qualifications, personal 
statement, CV and references. Applicants should have a good first degree in 
relevant engineering subjects or mathematics. Ideally the candidate should 
have some experience in 
aerodynamics and fluid structure interactions, but it is not necessary to 
have experience in machine learning to apply.

The successful applicant will be encouraged to further develop analytical 
and computational skills, work closely with team members, and submit the 
research results to high-quality 
journals. The project involves periods spent abroad at the partner 
organisation, based in Zurich. After successfully completing the PhD, the 
applicant will be well-prepared for a 
rewarding industrial or academic career, leveraging on the network of 
contacts created as part of the research project.

Aircraft noise, at take-off and landing, comes from the combination of (i) engine noise which, for modern turbofans, is generated by the fan and the jet, and (ii) airframe noise, which is mostly generated by LGs (Landing-gears) and HLDs (High-lift devices); the noise from HLDs consists of noise from leading edge slats and the trailing edge flaps, which are deployed at low speeds to increase lift. While engine noise remains dominant during take-off, airframe noise becomes a significant contributor during approach and landing, where engines are operated at low regime, especially for the most recent aircraft equipped with last generation turbofans. Therefore, mitigating airframe noise is of primary interest, even on modern aircraft and future aircraft designs.

However, due to strong integration constraints (among which weight is not the least) imposed by other disciplines than acoustics, the development and implementation of noise-reduction technologies (NRTs) on airframe components such as LGs and HLDs has been limited. The limited advancement in implementing NTRs is also due to the complex flow physics and our limited knowledge of the airframe noise generation mechanisms. Considering the complexity of the involved mechanisms, the challenge is to design low-noise airframe components based on multi-disciplinary criteria, acoustics being part of the performance criteria in conjunction with other significant aspects of flight physics.

The project

The postdoc work forms a part of an EU H2020 project addressing aircraft noise reduction with particular focus on airframe noise mitigation by means of innovative designs and noise-control technologies. Dedicated effort is required to achieve noise reduction based on CFD (Computational Fluid Dynamics) and CAA (Computational Aero-acoustics).

For the LG configuration, numerical simulation and modelling will be carried out to explore LG noise reduction with a perforated fairing. The planned activity is a continuation and refinement of previous effort, e.g., in the recent IMAGE project, for LG noise manipulation using wiremesh/fairing. The research will be carried out in close collaboration with other project partners. Instead of resolving the detailed flow past the fairing (mesh screen), a numerical model will be used, which will be verified against experimental data. This part of work aims at enabling efficient and reasonable CFD/CAA analysis, particularly, for the aero-acoustic assessment of whole-body aircraft configuration using high-fidelity hybrid RANS-LES methods together with acoustic analogy. For the HLD configuration, CFD/CAA analysis will be conducted on noise generation and propagation of a low-noise configuration with Krueger slat.

The numerical tools are STAR-CCM+, and/or the in-house M-Edge CFD solver together with in-house CAA tools (both are programmed in Fortran). Experience of using either of the above software, as well as Python/Matlab and Fortran are favored. The applicant must have good ability in reporting and disseminating his/her research results in English.

Project summary
The present project will address the yet-to-be-solved challenges related to 
model-parameter uncertainties and cost associated with fine tuning these 
parameters in Lattice Boltzmann models. The project will develop a machine-
learning(ML)-based approach that will allow for LB calibration.

This PhD project is part of a research project funded by the Australian Research 
Council (ARC) in collaboration with international experts from Australia, 
Europe, and the US.

The PhD student will join an international team of scientists dedicated to 
developing computational modelling for microfluidics and advancing knowledge of 
non-ideal fluid mixture behaviours that are critical for the rational design and 
robust optimisation of microfluidic applications. The project will be based in 
Queensland with collaborators spanning across Australia, Europe, and the US. 
Over three years, the successful applicants will have gained a broad range of 
technical skills and a sound understanding of computational microfluidics, 
positioning them ideally for a future career in a range of relevant disciplines.

Skills and experience:
1) Demonstrated knowledge and skills relevant to the thesis project and the 
subject of study
2) demonstrated knowledge in at least one of the following areas: computational 
fluid dynamics (CFD, in particular Lattice Boltzmann method), numerical methods, 
uncertainty quantification, statistical/Bayesian methods, machine learning, 
reduced order modelling, and applied and computational mathematical modelling, 
viscoelastic fluid flows
3) demonstrated programming skills (C++, Matlab, Python)
4) demonstrated written and oral communication skills with very good proficiency 
in English.
5) ability to work independently and to formulate and tackle research problems 
will be critical. Excellent organisational skills, be highly analytical, able to 
multitask under tight time frames will be considered highly.

The provision of a scholarship is conditional on successful application and 
admission to the Doctor of Philosophy course. Eligibility for admission to a 
research degree is determined by the Graduate Research Centre and processed via 
QUT's Application Portal (https://www.qut.edu.au/research/study-with-us/how-to-
apply)

Details: https://www.qut.edu.au/study/fees-and-scholarships/scholarships/phd-
scholarship-in-physics-informed-lattice-boltzmann-modelling

Contact: A./Prof. Emilie Sauret, emilie.sauret@qut.edu.au

Project summary: Miniaturisation is one of the most important current drivers 
for chemical and biological processes involved in gas sensing, water 
purification, cell culture and separation, and micro-reactors, among others. The 
lack of fundamental understanding of the microscopic fluid flow mechanisms, in 
particular the behaviour of non-ideal fluid mixtures, is a critical aspect 
underpinning the development of cutting-edge technologies. The present project 
will address the yet-to-be-solved challenges related to the cost of using 
Lattice Boltzmann (LB) approached in complex real-world microfluidic 
applications and devices. The project will develop machine-learning(ML)-based 
approach to create surrogate LB-based Reduced-Order Models that allow for fast 
and reliable numerical predictions, critical for employing the approach to 
realistic microfluidic applications.

This PhD project is part of a research project funded by the Australian Research 
Council (ARC) in collaboration with international experts from Australia, 
Europe, and the US, as well as industry partners.

The PhD student will join an international team of scientists dedicated to 
developing computational modelling for microfluidics and advancing knowledge of 
non-ideal fluid mixture behaviours that are critical for the rational design and 
robust optimisation of microfluidic applications.

Skills & experience:
Demonstrated knowledge and skills relevant to the thesis project and the subject 
of study
Demonstrated knowledge in at least one of the following areas: computational 
Lattice Boltzmann method, statistical/Bayesian methods, machine learning, 
reduced order modelling, and applied and computational mathematical modelling, 
viscoelastic fluid flows.
Demonstrated programming skills (C++, Matlab, Python)
Demonstrated written and oral communication skills with very good proficiency in 
English.
Ability to work independently and to formulate and tackle research problems will 
be critical. Excellent organisational skills, be highly analytical, able to 
multitask under tight time frames will be considered highly.

The provision of a scholarship is conditional on successful application and 
admission to the Doctor of Philosophy course. Eligibility for admission to a 
research degree is determined by the QUT Graduate Research Centre 
(https://www.qut.edu.au/research/study-with-us/how-to-apply)

https://www.qut.edu.au/study/fees-and-scholarships/scholarships/phd-scholarship-
in-lattice-boltzmann-based-reduced-order-modelling

Contact details: A/Prof. Emilie Sauret, emilie.sauret@qut.edu.au

Context 
Ocean flows at scales larger than few tens of km are quasi-horizontal due to the pronounced stratification of 
seawater and Earth’s rotation and are characterized by quasi-2D turbulence. At scales around 300 km (the 
mesoscale range), coherent structures (almost circular vortices) contain most of the kinetic energy and are 
key for ocean dynamics at climatic scales. At scales around 10 km (the submesoscale range) the flow is host 
to smaller eddies and filaments associated with strong gradients of physical properties (e.g. temperature) 
and intense vertical transport, which play an important role in both physical and biogeochemical budgets. 
Mesoscale and submesoscale flows also shape the physical and chemical environment in which life develops 
in the ocean. Direct observation of submesoscale surface velocity fields at global scale is still not possible 
but it should be achieved in the near future by the satellite SWOT (NASA-CNES, launch in 2022).
To compute large-scale horizontal transport, surface energy exchanges or global estimates of other 
quantities, it is crucial to assess how well the horizontal velocities provided by the satellite compare to actual 
surface currents and down to what length scale. For this purpose, Lagrangian approaches provide an ideal 
framework, as, differently from standard Eulerian ones, they integrate in time the signal. Thanks to this 
property, they may allow a clear separation between fast (ageostrophic) processes, that could contaminate 
the satellite-derived velocity, and slower (geostrophic) ones.

Work plan and goal 
In this thesis, inscribed in the CNES research project “DIEGO: Data and dynamical synergies for SWOT”, we 
will explore Lagrangian transport in models of surface ocean turbulence including ageostrophic dynamics by 
means of numerical simulations. In particular, we will focus on the role of the effective compressibility 
characterizing the 2D surface flows at scales of order 1 km, which is directly related to important vertical 
velocities. The research work will mainly rely on idealized simulations. Using the SWOT simulator software 
with the numerically computed flows, it will be possible to examine the effect of the data processing that will 
be applied to the real observations. Depending on the advancement of the project it could also be possible to 
use realistic high-resolution models, as well as the satellite data when available. The analysis will be based on 
the comparison of different statistical indicators of Lagrangian dispersion in the original and processed flows. 
The aim is to determine the effect of unresolved motions, and of the data processing procedure, on 
dispersion features. In particular, this study should allow the identification of a threshold length scale above 
which the approximate velocity field is accurate enough, at least in a statistical sense, as well as an estimate 
of the kinetic energy of the missing small scales.

Research team
The PhD thesis (starting in October 2021) will be conducted at UML, Lille, in tight collaboration with G. 
Lapeyre at LMD, ENS, Paris. It will also benefit from regular meetings with the staff of LOPS, Brest, involved 
on other workpackages of project DIEGO.

Candidate 
Candidate having good knowledge of fluid mechanics or dynamical systems and an interest for numerical 
methods; education: Master in Fluid Mechanics, Physics, Geophysical Fluid Dynamics, Applied Mathematics. 
Good knowledge of oral and written English is required. Knowing Fortran, Python or Matlab would be a plus.

Application
Interested candidates should send their CV, a letter of motivation, and possibly contact information of two 
references.

Open PD position in real-time simulations of polydisperse fluidized bed 
dynamics

The Department of Particulate Flow Modelling at the Johannes Kepler 
University, Austria, is recognized for developing novel modelling and 
simulation techniques for multiphase, multi-scale flows. With recurrence CFD 
(rCFD), we have created a data-assisted time-extrapolation methodology that 
allows to simulate recurrent processes several orders of magnitude faster 
than with conventional numerical tools.

For this joint project with the Hamburg University of Technology (TUHH), we 
are looking for a PostDoc who will further extend rCFD towards polydisperse 
particle dynamics. Under guidance of the rCFD core developers, the 
successful candidate will carry out real-time simulations of fluidized bed 
spray granulation, which will eventually be integrated into a control system 
for an experimental fluidized bed setup at TUHH.

Interested PostDoc applicants should have

• in-depth knowledge on the numerical simulation of multiphase (particulate) 
  flows

• pronounced programming skills (C/C++)
 
• a strong scientific track-record in terms of peer-reviewed publications

Our prospective colleague will be embedded in an international team of 15+ 
researchers with a strong focus on multi-scale modelling of multiphase 
flows. This project should start in summer/fall 2021 and is scheduled for 
three years. Salary will account to approx. 3.900 € gross per month 14 times 
a year.

Interested candidates are required to prepare a two-page application. The 
first page covers information on the applicant (name, photo, date of birth, 
email, academic career, titles of selected projects and publications) and 
the second page features an abstract of the applicant’s doctoral thesis (or 
equivalent research project). 

This short application is due on the 30th of April 2021 and should be sent 
by email (pdf file) to andrea.scharinger@jku.at. 

Opportunities for Graduate Studies related to Fluid Mechanics, Physics, 
Atmospheric Physics.

Type of positions: doctoral student (for applicants with master degree), direct-
doctoral program (for bachelor degree holder), or possibly master student. 
(Postdocs position also available)

Note! Except for postdoc positions, all prospective students are required to pass 
a standard mandarin language exam (HSK level-5, required by the Chinese 
government). 

Research area: Experimental, computational or theoretical physics/fluid mechanics 
related to turbulent flows, droplet/particle interaction with turbulence, 
microphysics of turbulent atmospheric cloud.

Brief introduction: Fluid turbulence is often quoted as the last unsolved problem 
in classical physics, a statement usually associated with R.P. Feynman (Nobel 
prize in Physics). Turbulent flows and interaction between turbulence and 
particle/droplets are at the heart of many processes in nature such as in the 
atmosphere (clouds, pollutant motion, weather-climate feedback etc.) and many 
engineering processes such as inside combustion engines, chemical and food 
processing, aircraft flights etc. Fundamental understanding of these subject is 
challenging and represents the forefront of scientific inquiry, owing to the non-
linear and complex nature of these problems. Our lab focuses on fundamental 
studies of turbulence and particle-turbulence interaction (with emphasis in either 
atmospheric or engineering processes) using cutting-edge experimental and/or 
computational methods and theoretical considerations. Sample projects: Experiment 
of droplet/particle motion and collisions in turbulence chamber via cutting-edge 
observation method or numerical simulation; direct numerical simulation and 
fundamental analysis of turbulent flows; designing new experimental tools or 
techniques to observe particle/droplet dynamics in atmospheric clouds; designing 
and/or conducting wind-tunnel experiments to observe pollution dispersion 
turbulent environments; develop A.I. drone for long-time deep-sea exploration, 
Machine learning simulation of turbulence etc. Exact project to be designed based 
on interest/ability 
of candidates.

About the institution: Sun Yat-Sen University is among the top-ten universities in 
China and is included in the ambitious national initiative by the Chinese 
Government to create a rank of “first class” universities by international 
standard. The position will be at the School of Atmospheric Science in the 
beautiful coastal Zhuhai campus. The School is a focal point of the current phase 
of development of SYSU and thus continuously receives generous investments. The 
city of Zhuhai, next to Macau and Hong Kong, is a top tourist destination in China 
thanks to its coastline and a “balanced” development.

About the research advisor: Ewe-Wei Saw, Malaysian citizen, Thousand-Young Talent 
professor at the School of Atmospheric Science, Sun Yat-Sen University (Zhuhai), 
obtained his PhD. in Physics from Michigan Tech. University (USA). He had previous 
worked at international research institutes such as Cornell University in the USA, 
Max Planck Institute in Germany and Commission of Atomic Energy (CEA), 
Observatoire de La Cote d’Azur in France (candidates may have possibility to 
collaborate with or further their career at these or other institutes). Dr. Saw’s 
research interest pertains to Fluid Dynamics of Turbulent Flows, Droplet/particle 
Dynamics in Turbulent Flows and Cloud Microphysics. During the years in Europe, 
Dr. Saw has extensive experience guiding and working with graduate students and 
postdocs. He has published research articles in respected scientific journals such 
as Nature-communications, Physical Review Letters, BAMS etc. Dr. Saw is a fluent 
speaker of English and Mandarin (among other) languages and have a flexible 
working style influenced by both European/American and Asian characteristics.

Requirements:
- Except for postdoc positions, all prospective students are required to pass a 
standard mandarin language exam (required by the Chinese government).

- Candidate’s background: Physics, Mechanics/mechanical engineering, Atmospheric 
science, Computational Science/engineering, or related field. Candidate with 
strong background in experimental, engineering work, programming (e.g. python, 
Fortran)  are encouraged to apply. Candidates without closely relevant background 
but could demonstrate strong interest or strong academic background or potential 
will also be seriously considered.

- Successful candidates should be a responsible, self-motivated person with keen 
interest in fundamental scientific research. Candidates should have decent 
communication and writing skills in English.

- For computationally inclined applicants, prior experience with numerical 
simulations (DNS, LES, lattice Boltzmann) of turbulence and/or parallel 
computation would be helpful.
Benefits:

- Standard remuneration for PhD students：full tuition + on campus hostel + 
allowance about RMB 1000--1200/month + possible additional allowance (depends on 
research funding situation).. For MS: full tuition + on campus hostel + allowance 
of about RMB 500--600/month + possible additional allowance (depends on research 
funding situation).. 

Contacts: For enquiries and to apply, please used the website's tool or email (in 
English or Mandarin) to zhoux288##mail.sysu.edu.cn (change ## to @, [dot] to "." 
).
Deadline: continuously hiring..

PhD Assistantship Positions

A PhD Assistantship is available in the Porous Media and Multiphase Flow Laboratory 
in the Department of Mechanical and Aerospace Engineering at NC State University.
  
The positions start in August 2021. The applicants must have a BS or a MS degree in 
Mechanical or Chemical Engineering, in addition to a strong background in Fluid 
Mechanics, Mathematics, and Computer Programming. 

Interested applicants are encouraged to contact Dr. Hooman Tafreshi 
(hvtafres@ncsu.edu) with the following information (all is needed):

1-Complete resume in PDF format,
2-Toefl score card, 
3-Transcripts,
4-Names and email address of three references.

Information regarding Dr. Tafreshi’s research can be found at using the links given 
below: https://www.mae.ncsu.edu/pmmf/

The Mechanical and Aerospace at NC State University has an enrollment of over 1,200 
undergraduates and 400 graduate students. The department is housed in Engineering 
Building III, a four-story, 250,000-square foot facility built in 2010. NC State’s 
location on Centennial Campus, combined with its proximity to the Research Triangle 
Park and neighboring universities, provides extensive opportunities for academic 
and industrial interaction and collaboration. The College of Engineering is ranked 
overall #24 and #9 in research expenditures among all engineering colleges in the 
nation.

Dassault Systèmes - a global innovator of simulation driven engineering 
solutions - is seeking an Aerospace CFD Engineer to help develop cutting-edge 
CFD simulation technology and software products that are used worldwide in 
aerospace and gas turbines industries. 

What will your role be?
As a member of the SIMULIA Fluids Research and Development Team, you will be 
responsible for verification, validation and facilitating development of novel 
Computational Fluid Dynamics (CFD) technologies for a broad range of aerospace 
high-speed flow applications. This position offers the opportunity to work with 
top researchers in the fields of Lattice Boltzmann and Turbulence Modeling, and 
working on innovative CFD technologies to impact real world industrial 
applications. 
A bit about the work environment:
•You will be working at the beautiful Dassault Systèmes Waltham campus.
•We develop in small collaborative teams where you will learn from others every 
day.
•We perform our CFD simulations with SIMULIA PowerFLOW; and implement our 
validation and verification processes primarily in python, C++, Perl, MySQL and 
bash scripts.
The challenges ahead
•We are particularly interested in candidates who have a solid background 
validating CFD technologies for high speed flows in aerospace and gas turbine 
applications
•Verify and validate CFD technology and product developments by carrying out 
technical research, setting up benchmark simulations, developing codes/scripts 
to automate process, writing-up analysis, and presenting results.
•Own the validation and verifications of new features from beginning to end, 
ensure that every validation and verifications delivered is reliable, 
performant, and on time.
•Play a key role in new product/technology development, interacting closely 
within the fluid physics and software development teams to transition product 
prototypes through all phases of the developments into product release.
•Act as a liaison between customers, R&D, and industry experience teams to 
analyze and diagnose issues related to our CFD products, provide assistance and 
support in using PowerFLOW for engineering development and beta testing.

Requirements:

•Masters or PhD in Physics, Mechanical Engineering, Aerospace Engineering or 
equivalent.
•Academic background and strong interest in fluid dynamics or physics 
•Thorough understanding of CFD, Fluid Dynamics, Turbulence Modeling,  with a 
focus on high speed flows in aerospace and gas turbine applications
•Validation and verification experiences with CFD software, experience with 
Lattice Boltzmann Methods (LBM) and familiarity with CFD software PowerFLOW is a 
plus
•Strong computer skills; familiarity with python, UNIX/Linux is a plus.
•Strong organizational and time management skills, effective English 
communication skills and good presentation skills.
•Excellent problem-solving and organizational skills, ability to thrive in a 
fast-paced, challenging environment  and a strong desire to learn
•Intellectual curiosity - regardless of your background, you enjoy the 
opportunity to continually learn new technologies and problem domains
•Self-motivated individual with a strong work ethic, detail oriented, ability to 
handle multiple tasks/multiple projects simultaneously

Valuable Additional Skills and Experience:
•Experiences in CFD applications of high lift aero-dynamics, shock-boundary 
layer interactions, gas turbine thermodynamics or jet acoustic noise 
•Experience with shell scripting and python programming is highly desired.
•Rudimentary experience in geometry handling and preparation.