Doctoral student in Optimal Transport for Optimization and Machine Learning Full-time

Doctoral Student In Optimal Transport For Optimization And Machine Learning

The project focuses on applying optimal transport and gradient flows to machine learning and optimization applications, such as deep generative models, sampling, inference, stochastic optimization, and beyond. The doctoral student will develop and analyze mathematical models and algorithms that connect partial (and/or stochastic) differential equations, infinite-dimensional optimization, and statistical machine learning. The goal is to build a theoretical and computational foundation for new methods in statistical inference and generative modeling, based on principled optimal transport and gradient flow theory.

We are looking for a highly motivated candidate with a strong background in mathematics, applied mathematics, or related fields, and with an interest in both theory and applications.

The doctoral student will be placed at the Department of Mathematics, School of Engineering Sciences (SCI), KTH Royal Institute of Technology, and will be part of a dynamic research environment with close collaboration with other researchers in the area.

Supervision: Jia-Jie Zhu is proposed to supervise the doctoral student. Decisions are made on admission.

To be admitted to postgraduate education, 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
• A master’s degree in mathematics, applied mathematics, optimization, statistics, machine learning, or a closely related field, with a solid background in analysis, differential equations, and/or optimization.
• Documented proficiency in mathematical reasoning and programming (e.g. Python) relevant for computational mathematics and machine learning is required.
• Experience in one or more of the following areas is considered advantageous: optimal transport, PDE, stochastic analysis, or theory of generative models, statistical inference and/or sampling

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

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.
• Experience in programming, numerical simulation is meritorious.
• Good communication skills in English as this will be required in day-to-day work and the ability to work both independently and in a team.
• Experience in working in international research environment

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

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 receive part of a workplace with many employee benefits and monthly salary according to KTH's Doctoral student salary agreement.