Artificial Intelligence (AI) is one of the central pillars of Industry 4.0 and Internet of Things, a global trend towards smart and highly automated factories.
AI empower manufacturing by representing physical equipment and processes with digital representations - Digital Twins - that allow production simulation, process monitoring, analytics and optimization.
Development of such AI solutions is complex and highly demanding. Moreover, existing AI solutions are often developed ad hoc and tailored to individual use cases.
This is not sufficient for the context of Industry 4.0. Thus, there is a pressing need for methods and systems to lower the complexity bar of AI development for industry and to make AI solutions more reusable, more accessible to a wider set of stakeholders and more collaborative.
Symbolic AI, including Knowledge Representation and Reasoning, Knowledge Graphs, ontologies and rules, allows to formally define Digital Twins and capture manufacturing domain knowledge, analytical routines, ML modules and pipelines.
Neuro AI, including Machine Learning (ML), knowledge embedding, and knowledge discovery, allows to enhance and adapt these Digital Twins to various application scenarios and to learn patterns in existing ML pipelines, generalize example sets of ML solutions or to search for the optimum in a space of parametrized ML solutions.
Such Hybrid AI approaches and Industry 4.0 context give a great variety of challenging research topics for PhD candidates.
The topics range from foundational to applied and give a great opportunity to do top-tier research and publish at top-tier venues as well as to test and evaluate novel techniques and methods in real industrial scenarios of Bosch.
g. Tensorflow / Pytorch), proven programming skills (e.g. Python / Java / C++), experience in writing scientific publications is a plus
You will be affiliated with the Bosch Center for Artificial Intelligence and with a top European university to obtain a PhD degree.
The final PhD topic is subject to your university. Duration : 3 years
Please submit all relevant documents (incl. curriculum vitae, transcripts, statement of interest and motivation letter).
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Kevin Heiner (Human Resources)
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Evgeny Kharlamov (Functional Department)