Doctoral student in probabilistic machine learning for fluid mechanics

Doctoral Student In Probabilistic Machine Learning For Fluid Mechanics

KTH Royal Institute of Technology, School of Engineering Sciences

Project description

Third-cycle subject: Engineering Mechanics

FLOW is a strong community of researchers engaged in fundamental and applied research in fluid mechanics within the department of Engineering Mechanics.

We are currently seeking a PhD student in probabilistic machine learning to join the Fluids and Surfaces Group within FLOW. AI is becoming a critical investment across science and engineering, but fluid mechanics poses particular challenges: flows are often high-dimensional and chaotic, and couple physical processes across many scales.

This position offers an exciting opportunity for a highly motivated candidate to develop trustworthy probabilistic methods for inference, optimization and data assimilation. These methods will integrate simulations and experimental data into reliable tools for the design, prediction and control of complex fluid systems.

The work focuses on turbulent flows that carry electric charge, as found for example in power transformers. These systems involve coupled processes that are only partly understood, and measurements are sparse and costly. This makes them an ideal testbed, where models must be calibrated, uncertainties quantified and experiments chosen with care, with direct impact on safer and more sustainable power infrastructure. The project is funded by Digital Futures och Hitachi Energy

Supervision: Shervin Bagheri and Ozan Öktem are proposed to supervise the student. Decisions are made on admission.

Admission requirements

To be admitted to postgraduate education (Chapter 7, 39 § Swedish Higher Education Ordinance), the applicant must have basic eligibility in accordance with either of the following:

• passed a second cycle degree (for example a master's degree), or
• completed course requirements of at least 240 higher education credits, of which at least 60 second-cycle higher education credits, or
• acquired, in some other way within or outside the country, substantially equivalent knowledge

In addition to the above, there is also a mandatory requirement for English equivalent to English B/6.

Selection

In order to succeed as a doctoral student at KTH you need to be goal oriented and persevering in your work. During the selection process, candidates will be assessed upon their ability to:

• independently pursue his or her work
• collaborate with others,
• have a professional approach and
• analyze and work with complex issues.

It is meritorious to have experience with fluid mechanics, machine learning and scientific computing, in particular, turbulent flows, Bayesian inference, probabilistic methods and generative AI

After the qualification requirements, great emphasis will be placed on personal skills.

Target degree: Doctoral degree

Information regarding admission and employment

Only those admitted to postgraduate education may be employed as a doctoral student. The total length of employment may not be longer than what corresponds to full-time doctoral education in four years' time. An employed doctoral student can, to a limited extent (maximum 20%), perform certain tasks within their role, e.g. training and administration. A new position as a doctoral student is for a maximum of one year, and then the employment may be renewed for a maximum of two years at a time. In the case of studies that are to be completed with a licentiate degree, the total period of employment may not be longer than what corresponds to full-time doctoral education for two years.

As a doctoral student, you are entitled to a workplace with many employee benefits and monthly salary according to KTH's Doctoral student salary agreement . Read more about Doctoral studies (PhD) | KTH | Sweden.

Tagged as: Engineering, Physics