RES ASSOC II (TEMP) - CSC

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How to Apply

Applications must include the following to be uploaded in one document:

  • Motivation letter: Describe your long-term research vision, the reason for applying for this job, and why you think you are the right candidate to fill this position (max. 2 pages)
  • Curriculum vitae including the list of publications
  • The names and contact details of at least two referees

Summary

Conduct research in the field of fake multimedia detection including deepfake detection. This project aims to develop cutting-edge technologies that combat the spread of synthetic media and maintain trust in digital content.  This position will contribute to the forefront of AI research, focusing on the identification and prevention of deepfake contents using state-of-the-art techniques such as multimodal AI and explainable AI.  The successful candidate will also participate in mentoring and supervising undergraduate and graduate students engaged in research activities.

Required Qualifications*

  • PhD in Computer Science, or equivalent with interest in digital forensics including multimodal data processing and deep learning with a track record of publications at top-tier conferences and/or high-impact journals in the field
  • Strong knowledge in Machine/Deep Learning with experience in multimodal AI, Explainable AI, and Neuro-symbolic AI
  • Interest in solving challenges related to Multimodal data fusion especially audiovisual modality, for explainable and robust AI models development
  • Hands-on experience with Deep Learning frameworks such as Keras and PyTorch
  • Excellent communication and presentation skills, including experience in communicating across discipline boundaries
  • Working in a Linux environment, with experience of shell scripting, cluster, or cloud computing

Desired Qualifications*

Individuals with expertise in handling multimodal (audiovisual) data using neurosymbolic reasoning models

Additional Information

University of Michigan-Flint - Plan for Diversity, Equity and Inclusion

The University of Michigan-Flint?s DEI plan can be found at: https://www.umflint.edu/dei/? 

The University of Michigan-Flint exhibits its commitment to diversity, equity, and inclusion through enacting fair practices, policies, and procedures particularly in support of the equitable participation of the historically underserved. UM-Flint recognizes the value of diversity in our efforts to provide equitable access and opportunities to all regardless of individual identities in support of a climate where everyone feels a sense of belonging, community, and agency.

Diversity is a core value at University of Michigan-Flint. We are passionate about building and sustaining an inclusive and equitable working and learning environment for all students, staff, and faculty. The University of Michigan-Flint seeks to recruit and retain a diverse workforce as a reflection of our commitment to serve the diverse people of Michigan, to maintain the excellence of the University, and to offer our students richly varied disciplines, perspectives, and ways of knowing and learning for the purpose of becoming global citizens in a connected world.

Background Screening

The University of Michigan conducts background checks on all job candidates upon acceptance of a contingent offer and may use a third party administrator to conduct background checks.  Background checks are performed in compliance with the Fair Credit Reporting Act.

Application Deadline

Job openings are posted for a minimum of three calendar days.  The review and selection process may begin as early as the fourth day after posting. This opening may be removed from posting boards and filled anytime after the minimum posting period has ended.

U-M EEO/AA Statement

The University of Michigan is an equal opportunity/affirmative action employer.