Simon Post-Doc Fellow (2025)

There can be several Fellow paths that a participants may follow applying for the Simon (Post-Doctoral) Fellow program:

• A participant with a PhD or MS/MA/MFA degree with a minimum of 5 years of industry-driven advanced data science or Senior Research Fellow (SRF) of Design Sciences may be directly admitted to the DESC FDS program.
• Alternatively, a participant can apply to a separate discipline-based MS/MA/MFA program, and seek admission to the DESC-SFDS program. After one semester of full-time enrollment in the Senior Fellow program, the participant can apply for admission to the DESC-SFDS program as a dual degree participant.
• Finally, a participant can apply for formal admission to the DESC-SFDS program without a master’s degree, but must complete 24 credits of ‘master’s equivalent’ coursework in addition to the 24 required credits for the DESC-SFDS Program. The choice of courses will be subject to approval by the Program Committee.

In all cases, DESC participants must have completed the Master’s degree by the time they reach doctoral candidacy in the program. A DESC participants are expected to have good statistical, mathematical and analytical skills to be able to conduct rigorous scientific research in design. A good minimal set of mathematical skills for all participants are equivalent to the material covered in the following courses: Calculus 1, Calculus 2, and Introduction to Probability and Statistics.

The increased computational capacity of machine learning and graph technologies and the diversity of the available Design Sciences applications in real-world industry initiatives have enabled data scientists, computer engineers and machine learning professionals to enhance the design sciences, either by making it more efficient or by expanding its conceptual boundaries. These methods empower participants to:

1. explore and evaluate complex problems;
2. create and deploy advanced fabrication and processing techniques; and
3. control the design process at different stages remarkably. Thus, the question is no longer whether Design Sciences are good or bad for digital products, services and functions but rather how can the discipline benefit from it.

The gradual appropriation of Design Science related terms by different authors has led to different definitions depending on the context and time period. Consequently, a considerable scope overlap exists between terms, which is the root of the ambiguous use of some Design Science-related terminologies by the design community. The teaching work is relevant and consistent Design Science terms and then establish computational reasoning, inferencing and knowledge representations that are consistent and promotes toward several representations. The teaching and research are critical for Product Design, Generative Intelligence, and Algorithmic Designs. As a leader, you will systematically discuss these terms and propose a clear and consistent path for participants. To ground such definitions, your work will trace their historical evolution, mapped the different perspectives found in the literature, and discussed the existent contradictions, ambiguities, and common intersections between those perspectives. The outcome is reasoning, inferencing and knowledge representation that provide a clear definition for each term, explains how they relate to each other, delimits their specific scopes, and maps their possible interactions.