Connectionists: 5 PhD positions in AI for semiconductor manufacturing

[Marcel van Gerven]

The AI Department at the Donders Institute is hiring 5 PhD students in AI for semiconductor manufacturing.

In this consortium project, we envision the creation of AI methods that will have a positive impact on the accuracy and reliability of smart industry, resulting in reducing waste of resources. Our partner for this project is Advanced Semiconductor Manufacturing Pacific Technologies (ASM-PT). You will be part of ROBUST, a new initiative by the Innovation Center for Artificial Intelligence (ICAI). ROBUST-ICAI, which includes 17 labs and 51 partners in public and private sectors, intends to hire 170 PhD candidates over the next ten years.

The general scientific challenge of the ASM ICAI lab is to develop AI models for real-time inference, prediction and control of high-throughput data streams within the semiconductor industry, ranging from low-level quality inspection to high-level process control. If you would like to know more about the ASM lab please visit: https://icai.ai/icai-labs/asm. All PhD candidates will be expected to contribute to ROBUST's sustainability goals, push forward the state of the art in machine learning, actively engage with the industrial partners,  reflect on the wider societal implications of their research, publish work in high-impact conferences and journals, be an active member of the ICAI consortium and a team player with excellent communication skills.

The PhD candidates will be supervised by Prof. Marcel van Gerven and several staff members from the AI Department at Radboud University. Projects will also include active involvement of ASM-PT staff. The five PhD positions will focus on the following objectives: PhD1 will focus on training generative multimodal deep learning models that can be deployed in the real world; PhD2 will focus on the development of data-efficient machine learning algorithms for real-time prediction and control of high-throughput systems; PhD3 will focus on the development of methods for neural architecture search to learn optimal (efficient, robust) controllers. Methods will be based on evolutionary strategies and reinforcement learning. PhD4 will investigate the use of neuromorphic computing as a future computing paradigm which may reduce energy consumption and maximise throughput of AI systems used in industry by orders of magnitude. PhD5 will develop a physics-inspired framework for trustworthy AI, which will provide explainable and efficient white-box solutions for several use cases in the semiconductor manufacturing industry.

We particularly encourage applications from underrepresented groups. You may apply here: https://lnkd.in/ewfPN5MU.


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