Postdoctoral position in Multimodal Deep Learning

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Are you interested in working with multimodal deep learning and computer vision, with the support of competent and friendly colleagues in an international environment? Are you looking for an employer that invests in sustainable employeeship and offers safe, favorable working conditions? We welcome you to apply for a postdoctoral research position at Uppsala University.

The Department of Information Technology holds a leading position in both research and education at all levels. We are currently Uppsala University's third largest department, have around 350 employees, including 120 teachers and 120 PhD students. Approximately 5,000 undergraduate students take one or more courses at the department each year. You can find more information about us on the Department of Information Technology website.

At the Division of Systems and Control, we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics, spanning diverse application domains such as medicine, energy systems, biomedical systems, neuroscience, and safety and security.

The Division of Systems and Control enjoys a wide network of strong international collaborators all around the world, for example at the University of Cambridge, University of Oxford, Imperial College, University of British Columbia, University of Sydney, University of Newcastle and Aalto University. We strive for all PhD students to get a solid international experience during their PhD.

The position is a part of the Beijer Laboratory for Artificial Intelligence Research funded by Kjell and Märta Beijer Foundation. The Beijer Laboratory for Artificial Intelligence Research was established in 2023 at Uppsala University with an ambition to grow activities within the subject of AI, focusing on applications in the life sciences and questions related to societal development.

Duties

Multimodal Vision-Language models integrate computer vision and natural language processing techniques to process and generate information that combines both visual and textual modalities, enabling a more profound understanding of the content within images and videos. While vision-language models exhibit promising potential, they are currently in early stages of development. Effective integration of both modalities (vision and language), and aligning visual and text embeddings into a cohesive embedding space, continues to pose significant challenges.

In this project, the successful candidate will conduct basic research and methods development towards designing efficient multimodal models and exploring their applications in computer vision. We are looking for a candidate with a deep learning background and an interest in working on the subject of vision-language modeling. The application areas of interest will be decided in a dialogue between the candidate and the supervisor, taking into account the candidate's interests and research proposal.

Technical keywords for the position include: deep learning, image analysis, representation learning and language modeling. The position might also include teaching up to 20%. Ability to teach in Swedish or English is required

The selected candidate will work in the Department of Information Technology, Division of Systems and Control, in Ekta Vats’ group.

Requirements

PhD degree in machine learning, deep learning, computer vision or another nearby and relevant field or a foreign degree equivalent to a PhD degree within machine learning,  deep learning, computer vision or another nearby and relevant field. The degree needs to be obtained by the time of the decision of employment. Those who have obtained a PhD degree three years prior to the application deadline are primarily considered for the employment. The starting point of the three-year frame period is the application deadline. Due to special circumstances, the degree may have been obtained earlier. The three-year period can be extended due to circumstances such as sick leave, parental leave, duties in labour unions, etc.

The applicant must have a strong background in method development and the use of machine learning. The ability to make use of deep learning is required. Publications at leading conferences in machine learning and computer vision is a strong plus.

Additional qualifications

Experience of interdisciplinary research is valuable. As a person, you are creative, thorough and have a structured approach. Great emphasis will be placed on personal characteristics and personal suitability.

Application

The application must contain:

  1. A curriculum vitae (CV).
  2. Transcript of PhD degree, including courses taken during PhD studies.
  3. A list of publications.
  4. Up to five selected publications in electronic format.
  5. A research statement describing your past and current research (max 1 page) and a proposal for future activities (max 1 page).
  6. Contact information for two references.
  7. A cover letter briefly describing your motivation for applying for this position and the earliest possible employment date (max 1 page). 

About the employment

The employment is a temporary position of 2 years according to central collective agreement. Full time position. Starting date 13 January 2025 or as agreed. Placement: Uppsala.

For further information about the position, please contact: Assistant Professor Ekta Vats, e-mail: ekta.vats@it.uu.se.

Please submit your application by 2 December 2024, UFV-PA 2024/3892

Are you considering moving to Sweden to work at Uppsala University? Find out more about what it´s like to work and live in Sweden.

Placement:
Department of Information Technology
Scope:
Full time
Working hours:
100%
Type of employment:
Temporary position
Pay:
Individual salary
Number of positions:
1
Town:
Uppsala
Union representatives:
Number of reference:
UFV-PA 2024/3892
Last application date:
9 December 2024
Apply for position

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