PhD in optimisation of compact heat exchangers at King’s College London

PhD opportunity to explore fluid topology optimisation for heat exchange systems using high-fidelity CFD.  
The goal of the work is to develop novel compact heat exchangers through combining the capabilities of 
additive manufacturing and the greater degree of freedoms offered by optimisation techniques.

Engineering applications:
-	Hydrogen fuel systems
-	Turbine blade cooling
-	Thermal management of electric parts

Applicants should have a strong skills or interest in CFD, fluid mechanics, numerical methods, and programming.

Are you adept at translating complex technical ideas into clear and accurate 
documentation? Do you have great attention to detail? Do you have the technical 
aptitude to learn to use our computational fluid dynamics (CFD) software?
 
If so, you might be a great addition to the documentation team here at 
Convergent Science. Convergent Science is an innovative, rapidly expanding CFD 
software company. We specialize in modeling turbulent reacting flows with our 
flagship product, CONVERGE, which is a revolutionary CFD software with truly 
autonomous meshing capabilities. Convergent Science is headquartered in Madison, 
Wisconsin, and has offices in the United States, Europe, and India.

We are looking for a technical writer to create and edit a wide variety of 
documents, including training slides, case setup guides, blog posts, and 
validation documents. You will work closely with software developers, support 
engineers, and other writers to craft documentation that is accurate, clear, 
concise, and useful. This job posting is for a full-time, in-person technical 
writing and documentation engineering position at our Madison, Wisconsin 
headquarters. Entry-level positions have the job title of “Engineer”, and 
higher-level positions have titles commensurate with experience.

Requirements
*Bachelor’s degree in a STEM field and technical writing experience OR writing-
related Bachelor's degree and a strong STEM background
***This position is often tailored to M.S. and Ph.D. levels of experience and 
subject matter expertise
*Meticulous and efficient writing and editing skills
*Ability to learn to use our graphical user interface-based CFD software
*Ability to learn from subject matter experts about CFD-related topics
*Expertise in Google products (sheets, docs) and Microsoft PowerPoint
*Knowledge of and experience with CFD is a plus

Benefits
Salary commensurate with experience
Possible annual bonus
Paid holidays, paid time off
Paid time off for company-approved volunteer activities
401k with employer match
Health, dental, and vision insurance
Short- and long-term disability insurance and life insurance

Convergent Science, Inc., is an Equal Opportunity Employer. We are committed to 
building a diverse team and to creating an inclusive environment for all 
employees. We believe that diversity and inclusion make our company stronger and 
our software better. All qualified applicants will receive consideration for 
employment without regard to race, color, religion, sex, sexual orientation, 
gender identity, national origin, disability, or status as a protected veteran.

Application Instructions

Interested applicants should send the following items to 
erik.tylczak@convergecfd.com. Incomplete applications may not receive a 
response.
Cover letter
Resume
Revised version of the user manual excerpt (see below)
Two samples of your technical writing

User manual excerpt (please revise for clarity and concision):
"In this present chapter, a description of the purpose of using the various 
different liquid spray breakup modeling techniques now available in the current 
CONVERGE code for the purposes of simulating the breakup of particles of a 
liquid nature in the domain can be found."

CONTEXT: 

The massive reliance on computational fluid dynamics (CFD) for research and engineering has led to the 

creation of massive and ever-growing fluid dynamics databases. These are significantly under-leveraged by 

historically low-dimensional approaches. The parallel with recent trends in AI, which point to learning 

techniques that improve by exploiting very large datasets using high-dimensional formulations, suggests that 

large gains can be made by overcoming these limitations. These alternative methods improve the extraction 

of essential and relevant information and significantly improve modeling approaches. Many data-driven fluid 

models have started to include a large number of local variables, meaning the road to higher dimensionality 

now lies with context-aware strategies, which can learn to automatically extract relevant information about 

the surrounding flow state and domain topology to refine their predictions. In this project, we explore the 

benefits and challenges provided by using context-aware learning techniques to extract predictive reduced-
order model (ROM)s.

In the context of flow simulations, ROMs represent approximate but cheap models of a physical phenomenon, 

allowing for fast prototyping and multiple queries. Model order reduction consists of restricting the search of 

a solution to a low-dimensional space spanned by a reduced-order basis. The latter is inferred from a set of 

precomputed high-fidelity solutions commonly using linear dimensionality reduction techniques, such as 

proper-orthogonal decomposition (i.e. principal component analysis), dynamic mode decomposition, etc. 

Despite important progress, most state-of-the-art ROMs fail to demonstrate robustness to uncertainties 

while remaining predictive when highly unsteady and turbulent systems are concerned. The main drawback in 

most reduction techniques is that reduced bases are learned using linear dimensionality reduction techniques 

and do not properly handle nonlinear problems which are commonplace in complex flow configurations. 

Recently, making use of recent advances in neural network architecture, nonlinear dimentionality reduction 

techniques have been infused in ROM community, using autoencoders and Convolutional (CNN) and Graph 
(GNN) neural networks.

However, novel approaches examine using mesh-free approaches, such as coordinate-based NNs to learn 

basis functions directly from the continuous vector field itself and not from its discretization while dynamics 

are modeled in a latent space. This hyper-reduction approach is discretization-independent and features 

lower memory consumption than prior discretization-dependent ROM approaches be they linear (PODs) or 

nonlinear (auto-encoders). In the same spirit, neural operators, which allow learning discretization-free 

representations, can be explored to build ROMs. These mesh-free methods will be explored in this project.



PROFILE:

The candidate should have a MSc degree or equivalent in fluid mechanics or applied mathematics, with 

experience in scientific computing.



SKILLS:

Programming experience and expertise in data-driven techniques will be considered very positively.



DURATION & START DATE:

The position is offered for the duration of 36 months, between October 1st, 2024 and September 30th, 2027. 



REQUIRED DOCUMENTS:

The applicant should include a CV, and a motivation letter.



CONTACT:

taraneh.sayadi@lecnam.net



REFERENCES:

[1] Johannes Brandstetter, Daniel E. Worrall, and Max Welling. “Message Passing Neural PDE Solvers”. In: 

International Conference on Learning Representations. 2022. 

[2] Rizal Fathony, Anit Kumar Sahu, Devin Willmott, and J Zico Kolter. “Multiplicative filter 

networks”. In: International Conference on Learning Representations. 2021.

[3] Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein. “Implicit neural 

repre- sentations with periodic activation functions”. In: Advances in Neural Information Processing Systems 

33 (2020), pp. 7462–7473.

[4] Peter Yichen Chen et al. “CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural 

Representations”. In: ICLR 2023. 2022. url: http://arxiv.org/abs/2206.02607.

[5] Zongyi Li et al. “Fourier Neural Operator for Parametric Partial Differential Equations”. In: ICLR. 2021.

[6] C. Scherding, G. Rigas, D. Sipp, P.J. Schmid, and T. Sayadi, Ronaalp: Reduced-order nonlinear 

approximation with active learning procedure, 2023.

[7] C. Scherding, G. Rigas, D. Sipp, P. J. Schmid, and T. Sayadi, Data-driven framework for input/output 

lookup tables reduction: Application to hypersonic flows in chemical nonequilibrium, Phys. Rev. Fluids, 8 

(2023), p.023201 [8] S. Kneer, T. Sayadi, D. Sipp, P. Schmid, and G. Rigas, Symmetry-aware autoencoders: s-

pca and s-nlpca, arXiv, (2021), p. 2111.02893v2

Workplace


The Centre on Sustainable Energy (SE) supports the development of devices and 
methods for generation, storage, and distribution of energy solutions at low 
environmental impact. This will be done in the perspective of energy 
sustainability, of systems and solutions that respect the environment and the 
quality of life, of solutions with a lower impact on health and on environmental 
pollution levels. The ground for the Centre on “Sustainable Energy” is based on 
the Decarbonization targets, which will demand for more flexibility of the 
energy system, through new gas and power grids and using energy vectors and 
storage solutions, as enablers for the wide penetration of renewables.

FBK actively seeks diversity and inclusion in the workplace and is also 
committed in promoting gender equality.  


Job Description

The position offered concerns mainly the development and validation of chemical 
and/or electrochemical models related to Lithium-ion battery recycling processes 
and battery cells. The candidate will provide support to the Battery and 
Electrification Technologies (BET) unit in both European and national projects 
related to Li-ion batteries and redox flow batteries.  The selected candidate 
will work in the FREE4LIB Horizon Europe project and contribute to the European 
Battery Innovation IPCEI project (https://www.ipcei-batteries.eu) aiming at the 
development of next-generation battery technologies.

The activities of the successful candidate will focus on:
development and validation of models for the different chemical processes 
involved in Li-ion battery recycling (e.g., leaching, precipitation, liquid-
liquid extraction, etc);
development and validation of electrochemical models at cell level for different 
battery technologies (e.g., Li-ion, Redox flow batteries, Solid-state 
batteries);
supporting cell testing activities that will provide inputs for the models.

The ideal candidate should have:
MSc in Chemical Engineering, Computational Engineering or equivalent technical 
background;
Strong know-how on chemical and/or electrochemical processes;
Solid know-how on computational methods for the resolution of PDEs and ODEs;
Know-how on modelling chemical or electrochemical phenomena at component or 
system level, preferably using opensource or custom-developed software;
Skills in programming (e.g., C++, python or Julia) and some experience in the 
development of applications or libraries for modelling physical systems;
Experience with computational tools for multiphysics analyses (e.g., OpenFOAM, 
FeniCS, Ansys Fluent) and/or with system level/chemical process simulators 
(e.g., OpenModelica, Aspen, DWSim);
Good knowledge of written and spoken English;
Ability to work in a collaborative environment, with a strong commitment to 
achieve assigned objectives;
Ability to organize and complete multiple tasks/projects at one time;
Skills in problem solving;
Ability to write technical reports and scientific papers;
Good communication and relational skills;
Self-motivation and result orientation.

Furthermore, the following elements will be positively evaluated:
PhD degree (or a few years of experience) in fields related to this call;
Solid know-how on electrochemistry and batteries;
Know-how on battery cells electrochemical models for Li-ion (e.g., SPM, DFN) or 
redox flow batteries.

Employment


Type of contract: Fixed Term Contract 
Gross annual salary:about € 36.200 - € 41.400 depending on background and 
expertise in the field.
Working hours: full time (38 hours per week)
Start date: July 2024 
Duration: 2 years (with the possibility to extend the contract)
Workplace: Povo - Rovereto 
Benefits: flexi-time, company subsidized cafeteria or meal vouchers, internal 
car park, welcome office support for visa formalities, accommodation, social 
security (SANIFONDS), family-work balance, free training courses, support on 
bank account opening, discount on public transport, sport, language course fees. 
More info at  https://www.fbk.eu/en/work-with-us/  

Application
Interested candidates are requested to submit their application by completing 
the online form (https://jobs.fbk.eu/). Please make sure that your application 
contains the following attachments (in pdf format):

Detailed CV including a list of scientific publications;
Cover Letter (explaining your motivation for this specific position)
At least 2 professional references (e-mails and/or phone numbers)

Application deadline: May 21, 2024


Please read our Regulations on the recruitment and selection of fixed-term 
personnel (effective from October 15, 2018) before completing your application.
For further information, please contact the Human Resources Services at 
jobs@fbk.eu. 

This position can be remote if the candidate does not live in United States or 
India. 

Tridiagonal Software is a leading Software OEM developing and licensing highly 
automated Engineering Analysis tools powered by CFD to the global chemicals and 
pharmaceutical industry. Our flagship products MixIT and SimSight are licensed 
by some of the World’s largest corporations based in US and Europe. 
(https://mixing-solution.com/) and (https://simsight.tridiagonalsoftware.com/) 
We are on a fast-track growth mode and are looking for young, dynamic people to 
join this exciting journey with us to contribute to their own and company’s 
growth. We provide an accelerated career growth path to individuals with 
tremendous opportunity to learn, execute and excel. 

Essential Duties and Responsibilities:
•	LBM solver development for software products and services
•	Development and maintenance of solver
•	Adding new physics model to existing solvers
•	Development of solution strategy for customer specific problems
•	Work on file writing, GUI development for solver 
•	Work with development team, product management and QA
•	Participate in internal and customer meetings

Key Competencies:
•	Willingness to be part of software development team, developing the next 
        generation solutions for manufacturing & chemical industry using the 
        latest technologies
•	Good understanding of basic fluid dynamics and heat transfer
•	Exposure to reaction modelling, multiphase modelling or compressible 
        flows will be an added advantage
•	Formal course work and good understanding of numerical methods and 
        computational fluid dynamics (CFD) is a critical requirement
•	Experience/project work in LBM solver development required
•	Proficiency in any one of programming language like C, C++ or Fortran 
        etc. (C++ is preferred)
•	Good verbal and written communication skills
•	Strong Presentation Skills Knowledge 
•	Willing to learn new tools and technologies
•	Should be a self-starter, quick learner with sense of project ownership

Education & Experience:
•	Ph.D. / M. E. / MTech in Mechanical / Chemical/ Aerospace/ Thermal 
        Engineering having keen interest in Solver development with 0 to 3 years 
        of experience. 
•	Master’s or Ph.D. project in CFD solver development preferred.



Remuneration: 
•	Compensation as per industry standards. 
•	Benefits would be detailed in the final offer.

Contact Details:  Christina Castillo – christina.castillo@tridiagonal.com

Job Title: Fully Funded PhD Position in Fluid-Structure Interaction Modeling with SPH

Institution: Sabancı University, Istanbul, Turkey

About Us: We are a dynamic research group led by Prof. Dr. Mehmet Yıldız, dedicated to pioneering research in a wide spectrum of areas in materials science, mechanical engineering and ocean engineering. Our group focuses on the development of particle-based numerical tools such as Smoothed Particle Hydrodynamics (SPH) and Peridynamics, in wide ranges of complex topics including free surface flow, multi-phase flow, non-Newtonian flows, electrohydrodynamics, magnetohydrodynamics, solid mechanics with damage modeling, fluid-structure interaction, and also extends into exciting novel areas like active matters.

Position Description:

We are seeking a highly motivated and talented PhD researcher to join our research team in the field of Smoothed Particle Hydrodynamics (SPH). The successful candidate will work on cutting-edge research projects related to Fluid-Structure Interaction Modeling with SPH method.

Responsibilities:

• Conduct in-depth research on SPH methodologies and their applications.

• Develop and implement SPH-based numerical simulations.

• Collaborate with other team members and contribute to interdisciplinary projects.

• Publish research findings in peer-reviewed journals and present at conferences.

Requirements:

• A Master's degree or equivalent in a relevant field (e.g., mechanical engineering, marine engineering, applied mathematics etc.).

• Strong background in computational fluid dynamics or numerical simulations.

• Proficiency in at least one of the programming languages commonly used in CFD simulations (C++, Fortran, CUDA, OpenCL, OpenMP/MPI, Python).

• Excellent problem-solving skills and a passion for scientific research.

• Effective written and oral communication skills in English.

What We Offer:

• A stimulating and collaborative research environment.

• Access to state-of-the-art computational and experimental resources of the Integrated Manufacturing Technologies Research and Application Center (SU-IMC) of Sabancı University.

• Opportunities for professional development, including attending conferences and workshops.

• The PhD position in Sabancı University will be fully funded; this will include tuition fee, campus accommodation, and the monthly stipend.

Application Process:

Interested candidates should submit the following documents as a single PDF file to the email address: denizcan.kolukisa@sabanciuniv.edu, including roozbeh.saghatchi@sabanciuniv.edu and mehmet.yildiz@sabanciuniv.edu in email CC.

1. A cover letter explaining their motivation for pursuing a PhD in Fluid-Structure Interaction Modeling with SPH and their research interests.

2. A detailed CV, including academic qualifications, research experience, and publications (if any).

3. Contact information for at least two academic or professional references.

Important Dates:

• Application Deadline: 24 May 2024

• Shortlisted candidates will be contacted for interviews.

Additional Information:

• For inquiries or further information about the position, please contact denizcan.kolukisa@sabanciuniv.edu and roozbeh.saghatchi@sabanciuniv.edu.

• Please note that TOEFL and GRE scores are required to be enrolled in Sabanci University graduate programs (Scores must be acquired before the enrollment application deadline on 31 May 2024). https://www.sabanciuniv.edu/en/admission-to-graduate-programs

Sabancı University is committed to promoting diversity and equal opportunity in education and employment. We welcome applications from candidates of all backgrounds and experiences.

Job Title: Fully Funded PhD Position in Multiphase Flow Modeling with SPH and Phase Field Methods

Institution: Sabancı University, Istanbul, Turkey

About Us: We are a dynamic research group led by Prof. Dr. Mehmet Yıldız, dedicated to pioneering research in a wide spectrum of areas in materials science, mechanical engineering and ocean engineering. Our group focuses on the development of particle-based numerical tools such as Smoothed Particle Hydrodynamics (SPH) and Peridynamics, in wide ranges of complex topics including free surface flow, multi-phase flow, non-Newtonian flows, electrohydrodynamics, magnetohydrodynamics, solid mechanics with damage modeling, fluid-structure interaction, and also extends into exciting novel areas like active matters.

Position Description:

We are seeking a highly motivated and talented PhD researcher to join our research team in the field of Smoothed Particle Hydrodynamics (SPH). The successful candidate will work on cutting-edge research projects related to Multiphase Flow Modeling with SPH and Phase Field Methods.

Responsibilities:

• Conduct in-depth research on SPH and Phase Field methodologies and their applications.

• Develop and implement numerical simulations combining the SPH and Phase Field methods.

• Collaborate with other team members and contribute to interdisciplinary projects.

• Publish research findings in peer-reviewed journals and present at conferences.

Requirements:

• A Master's degree or equivalent in a relevant field (e.g., mechanical engineering, marine engineering, materials engineering, applied mathematics etc.).

• Strong background in computational fluid dynamics or numerical simulations.

• Proficiency in at least one of the programming languages commonly used in CFD simulations (C++, Fortran, CUDA, OpenCL, OpenMP/MPI, Python).

• Excellent problem-solving skills and a passion for scientific research.

• Effective written and oral communication skills in English.

What We Offer:

• A stimulating and collaborative research environment.

• Access to state-of-the-art computational and experimental resources of the Integrated Manufacturing Technologies Research and Application Center (SU-IMC) of Sabancı University.

• Opportunities for professional development, including attending conferences and workshops.

• The PhD position in Sabancı University will be fully funded; this will include tuition fee, campus accommodation, and the monthly stipend.

Application Process:

Interested candidates should submit the following documents as a single PDF file to the email address: denizcan.kolukisa@sabanciuniv.edu, including roozbeh.saghatchi@sabanciuniv.edu and mehmet.yildiz@sabanciuniv.edu in email CC.

1. A cover letter explaining their motivation for pursuing a PhD in Multiphase Flow Modeling with SPH and Phase Field Methods and their research interests.

2. A detailed CV, including academic qualifications, research experience, and publications (if any).

3. Contact information for at least two academic or professional references.

Important Dates:

• Application Deadline: 24 May 2024

• Shortlisted candidates will be contacted for interviews.

Additional Information:

• For inquiries or further information about the position, please contact denizcan.kolukisa@sabanciuniv.edu and roozbeh.saghatchi@sabanciuniv.edu.

• Please note that TOEFL and GRE scores are required to be enrolled in Sabanci University graduate programs (Scores must be acquired before the enrollment application deadline on 31 May 2024). https://www.sabanciuniv.edu/en/admission-to-graduate-programs

Sabancı University is committed to promoting diversity and equal opportunity in education and employment. We welcome applications from candidates of all backgrounds and experiences.

Job Title: Fully Funded PhD Position in Failure Modeling of Composites with SPH

Institution: Sabancı University, Istanbul, Turkey

About Us: We are a dynamic research group led by Prof. Dr. Mehmet Yıldız, dedicated to pioneering research in a wide spectrum of areas in materials science, mechanical engineering and ocean engineering. Our group focuses on the development of particle-based numerical tools such as Smoothed Particle Hydrodynamics (SPH) and Peridynamics, in wide ranges of complex topics including free surface flow, multi-phase flow, non-Newtonian flows, electrohydrodynamics, magnetohydrodynamics, solid mechanics with damage modeling, fluid-structure interaction, and also extends into exciting novel areas like active matters.

Position Description:

We are seeking a highly motivated and talented PhD researcher to join our research team in the field of Smoothed Particle Hydrodynamics (SPH). The successful candidate will work on cutting-edge research projects related to Failure Modeling of Composites with SPH method.

Responsibilities:

• Conduct in-depth research on SPH methodologies and their applications.

• Develop and implement SPH-based numerical simulations.

• Collaborate with other team members and contribute to interdisciplinary projects.

• Publish research findings in peer-reviewed journals and present at conferences.

Requirements:

• A Master's degree or equivalent in a relevant field (e.g., mechanical engineering, materials engineering, applied mathematics etc.).

• Strong background in solid mechanics or numerical simulations.

• Proficiency in at least one of the programming languages commonly used in structural mechanics simulations (C++, Fortran, CUDA, OpenCL, OpenMP/MPI, Python).

• Excellent problem-solving skills and a passion for scientific research.

• Effective written and oral communication skills in English.

What We Offer:

• A stimulating and collaborative research environment.

• Access to state-of-the-art computational and experimental resources of the Integrated Manufacturing Technologies Research and Application Center (SU-IMC) of Sabancı University.

• Opportunities for professional development, including attending conferences and workshops.

• The PhD position in Sabancı University will be fully funded; this will include tuition fee, campus accommodation, and the monthly stipend.

Application Process:

Interested candidates should submit the following documents as a single PDF file to the email address: denizcan.kolukisa@sabanciuniv.edu, including roozbeh.saghatchi@sabanciuniv.edu and mehmet.yildiz@sabanciuniv.edu in email CC.

1. A cover letter explaining their motivation for pursuing a PhD in Failure Modeling of Composites with SPH and their research interests.

2. A detailed CV, including academic qualifications, research experience, and publications (if any).

3. Contact information for at least two academic or professional references.

Important Dates:

• Application Deadline: 24 May 2024

• Shortlisted candidates will be contacted for interviews.

Additional Information:

• For inquiries or further information about the position, please contact denizcan.kolukisa@sabanciuniv.edu and roozbeh.saghatchi@sabanciuniv.edu.

• Please note that TOEFL and GRE scores are required to be enrolled in Sabanci University graduate programs (Scores must be acquired before the enrollment application deadline on 31 May 2024). https://www.sabanciuniv.edu/en/admission-to-graduate-programs

Sabancı University is committed to promoting diversity and equal opportunity in education and employment. We welcome applications from candidates of all backgrounds and experiences.

Job Title: Fully Funded PhD Position in Modeling Cold Spray Printing of Materials with SPH

Institution: Sabancı University, Istanbul, Turkey

About Us: We are a dynamic research group led by Prof. Dr. Mehmet Yıldız, dedicated to pioneering research in a wide spectrum of areas in materials science, mechanical engineering and ocean engineering. Our group focuses on the development of particle-based numerical tools such as Smoothed Particle Hydrodynamics (SPH) and Peridynamics, in wide ranges of complex topics including free surface flow, multi-phase flow, non-Newtonian flows, electrohydrodynamics, magnetohydrodynamics, solid mechanics with damage modeling, fluid-structure interaction, and also extends into exciting novel areas like active matters.

Position Description:

We are seeking a highly motivated and talented PhD researcher to join our research team in the field of Smoothed Particle Hydrodynamics (SPH). The successful candidate will work on cutting-edge research projects related to SPH modeling and simulations in Cold Spray Printing of Materials.

Responsibilities:

• Conduct in-depth research on SPH methodologies and their applications.

• Develop and implement SPH-based numerical simulations.

• Collaborate with other team members and contribute to interdisciplinary projects.

• Publish research findings in peer-reviewed journals and present at conferences.

Requirements:

• A Master's degree or equivalent in a relevant field (e.g., mechanical engineering, materials engineering, applied mathematics, etc.).

• Strong background in computational fluid dynamics or numerical simulations.

• Proficiency in at least one of the programming languages commonly used in CFD simulations (C++, Fortran, CUDA, OpenCL, OpenMP/MPI, Python).

• Excellent problem-solving skills and a passion for scientific research.

• Effective written and oral communication skills in English.

What We Offer:

• A stimulating and collaborative research environment.

• Access to state-of-the-art computational and experimental resources of the Integrated Manufacturing Technologies Research and Application Center (SU-IMC) of Sabancı University.

• Opportunities for professional development, including attending conferences and workshops.

• The PhD position in Sabancı University will be fully funded; this will include tuition fee, campus accommodation, and the monthly stipend.

Application Process:

Interested candidates should submit the following documents as a single PDF file to the email address: denizcan.kolukisa@sabanciuniv.edu, including roozbeh.saghatchi@sabanciuniv.edu and mehmet.yildiz@sabanciuniv.edu in email CC.

1. A cover letter explaining their motivation for pursuing a PhD in Modeling Cold Spray Printing of Materials with SPH and their research interests.

2. A detailed CV, including academic qualifications, research experience, and publications (if any).

3. Contact information for at least two academic or professional references.

Important Dates:

• Application Deadline: May 24, 2024

• Shortlisted candidates will be contacted for interviews.

Additional Information:

• For inquiries or further information about the position, please contact denizcan.kolukisa@sabanciuniv.edu and roozbeh.saghatchi@sabanciuniv.edu.

• Please note that TOEFL and GRE scores are required to be enrolled in Sabanci University graduate programs (Scores must be acquired before the enrollment application deadline on May 31, 2024). https://www.sabanciuniv.edu/en/admission-to-graduate-programs

Sabancı University is committed to promoting diversity and equal opportunity in education and employment. We welcome applications from candidates of all backgrounds and experiences.