Doctoral Thesis - Supercomputer quantum inspired optimization solutions for at Infineon Technologies AG Australia

Dresden, Saxony, Germany -

Full Time

Start Date

Immediate

Expiry Date

12 Feb, 26

Salary

0.0

Posted On

14 Nov, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Applied Mathematics, Physics, Computer Science, Programming, C++, Python, PyTorch, Optimization, Quantum Computing, Neuromorphic Computing, Data Analysis, Algorithm Development, Manufacturing, Digitalization, Simulation, Machine Learning

Industry

Semiconductor Manufacturing

Description

As an industrial doctorate at Infineon, you will pursue a doctoral degree at a University and gain professional experience simultaneously - an ideal start for your career. Advance your research with us and profit from our vast network of doctoral candidates and the expertise of a university. Mentorship is handled by both professors and dedicated Infineon employees. The research is carried out in cooperation with the University of Technical University Dresden and under the supervision of Prof. Dr.-Ing. habil. Dr. sc. nat. Christian Mayr. In the "Digital Manufacturing" department at Infineon Dresden, your goal is to advance the digitalization of manufacturing as well as the networking and optimization of production areas. This involves using innovative mathematical methods to maximize the benefits of the vast amount of existing data and to make material-flow in the production-line or in the global manufacturing network more efficient and faster. To master optimization problems in semiconductor production is key for business success. Such NP-hard challenges are bringing even today's conventional supercomputers towards its limits with billions of parameters to optimize. Therefor alternative approaches like quantum computers, able to run extreme parallelism using quantum superposition, are considered, but technically challenging itself. Less well-known neuromorphic compute approaches, however, have already demonstrated their capabilities for distributed processing and simulation approaches. Here we therefore combine this strength on SpiNNaker2 as a "quantumorphic" system, i.e. a system that, while not a quantum computer, shares certain characteristics with it, using SpiNNaker2 for physics/quantum-inspired algorithms. It has already shown better scaling than either quantum computers or conventional supercomputers for quadratic unconstrained binary optimization [1]. Another example could be stochastic spiking neurons for solving finite-element tessellations in a highly parallel, asynchronous fashion [2], or highly parallel Monte-Carlo Sampling. Literature: [1] Chen, Zihao, et al. "ON-OFF neuromorphic ISING machines using Fowler-Nordheim annealers." Nature communications 16.1 (2025): 3086. [2] Theilman, Bradley H., and James B. Aimone. "Solving Sparse Finite Element Problems on Neuromorphic Hardware." arXiv preprint arXiv:2501.10526 (2025). A tentative work plan could e.g. focus on using QUBO and other heuristics for industrial optimization. Analyzing the state of the art: Analyze current approaches and their fit on SpiNNaker2 Identifying potential for improvement: Choose a single algorithm or small subset for optimization and implementation, plus a sample application to industrial optimization Implementation: Complete a full processing chain from use case to implementation e.g. as a Education: Master's Degree (or equivalent) in applied mathematics, physics, computer science or related fields of expertise Programming: Very good programming skills (e.g. C++, Python, PyTorch) Experience: Excellent skills and practical experience in one or more of the following research areas are mandatory: We are on a journey to create the best Infineon for everyone. This means we embrace diversity and inclusion and welcome everyone for who they are. At Infineon, we offer a working environment characterized by trust, openness, respect and tolerance and are committed to give all applicants and employees equal opportunities. We base our recruiting decisions on the applicant´s experience and skills.

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Responsibilities

The role involves advancing digitalization and optimization in semiconductor production using innovative mathematical methods. The candidate will work on optimization problems and implement algorithms for industrial applications.