Doctoral student in Generative Modeling for Complex Data - Hiring in process/Finished, not possible to apply

Project description

Third-cycle subject: Applied and Computational Mathematics

We are seeking a motivated doctoral student to work on generative modeling for time series and sequential data. The project focuses on developing principled, scalable, and efficient methods for modeling complex temporal dynamics, with applications in probabilistic forecasting and data imputation. Possible research topics include (but are not limited to) developing principled frameworks for flow matching and related generative models for sequential data, designing structured priors to enhance generative modeling methods, developing scalable yet principled methods for modeling long and high-dimensional spatiotemporal data, and exploring applications across scientific and applied domains.

This position is a joint appointment between KTH Mathematics and Nordita under the Wallenberg Initiative for Networks and Quantum (WINQ), offering a stimulating environment at the interface of mathematical statistics, machine learning, and the physics of complex systems.

Supervision: Soon Hoe Lim (primary supervisor), Henrik Hult (co-supervisor) is proposed to supervise the doctoral student. Decisions are made on admission

What we offer

• The possibility to study in a dynamic and international research environment in collaboration with industries and prominent universities from all over the world.
• A workplace with many employee benefits and monthly salary according to KTH’s Doctoral student salary agreement.
• A postgraduate education at an institution that is active and supportive in matters pertaining to working conditions, gender equality and diversity as well as study environment.
• Work and study in Stockholm, close to nature and the water.
• Guidance on relocating and settling in at KTH and in Sweden

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

We encourage applications from candidates with a strong background in statistical machine learning and prior research experience in generative modeling for time series.

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.
• demonstrate prior research experience and good programming skills.

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.

Union representatives

Contact information for union representatives.

Doctoral section (Students’ union on KTH Royal Institute of Technology)

Contact information for doctoral section.

To apply for the position

Apply for the position and admission through KTH's recruitment system. It is the applicant’s responsibility to ensure that the application is complete in accordance with the instructions in the advertisement.

Applications must be received at the last closing date at midnight, CET/CEST (Central European Time/Central European Summer Time).

Applications must include the following elements:

• CV including your relevant professional experience and knowledge.
• Application letter with a brief description of why you want to pursue research studies, about what your academic interests are and how they relate to your previous studies and future goals. (Maximum 2 pages long)
• Copies of diplomas and grades from previous university studies and certificates of fulfilled language requirements (see above). Translations into English or Swedish if the original document is not issued in one of these languages.Copies of originals must be certified.
• Representative publications or technical reports. For longer documents, please provide a summary (abstract) and a web link to the full text.

Other information

Striving towards gender equality, diversity and equal conditions is both a question of quality for KTH and a given part of our values.

For information about processing of personal data in the recruitment process.

It may be the case that a position at KTH is classified as a security-sensitive role in accordance with the Protective Security Act (2018:585). If this applies to the specific position, a security clearance will be conducted for the applicant in accordance with the same law with the applicant's consent. In such cases, a prerequisite for employment is that the applicant is approved following the security clearance.

We firmly decline all contact with staffing and recruitment agencies and job ad salespersons.

Disclaimer: In case of discrepancy between the Swedish original and the English translation of the job announcement, the Swedish version takes precedence.