PhD - Antwerpen | More than two weeks ago
Many real-world problems contain for (potentially a large part) uncertainty and incomplete information. Despite the impressive success of AI methods across many applications, these methods still have difficulties in those uncertain situations or contexts with missing information. Probabilistic computing is a field study that aims to incorporate these uncertainties in its models and make probabilistic predictions or decisions [1, 2]. Techniques of interest, are among others, Bayesian networks and Markov models. In this research topic we want to investigate the impact of probabilistic compute paradigms and novel hard ware implementations on the algorithmic level. A first step will be to research different emulation strategies of those hardware paradigms in software as a starting point to benchmark different architectures and algorithms. Later on, we can study the impact at the different layers of different algorithms or AI models. The learnings from the probabilistic area can potentially be transferred to other hardware efficient and edge AI algorithms or paradigms in a later stage of this PhD.
We offer you a challenging, stimulating and pleasant research environment, where you can contribute to international research on artificial intelligence with a close link to the underlaying hardware. While you will work in the AI & Data department, you will also be working together with imec hardware and university teams to jointly come up with a full stack solution.
Our ideal candidate for this position has the following skills:
 Kaiser, J., & Datta, S. (2021). Probabilistic computing with p-bits. Applied Physics Letters, 119(15).
 Misra, S., Bland, L. C., Cardwell, S. G., Incorvia, J. A. C., James, C. D., Kent, A. D., ... & Aimone, J. B. (2022). Probabilistic neural computing with stochastic devices. Advanced Materials, 2204569.
Required background: Master’s degree in Computer Science, Informatics, Physics, Engineering or Electronics, with knowledge about artificial intelligence and machine learning
Type of work: Modelling, algorithmic and system design, experimentation, literature study
Supervisor: Steven Latré
Co-supervisor: Tom De Schepper
Daily advisor: Julie Moeyersoms
The reference code for this position is 2024-092. Mention this reference code on your application form.