126 electrical-machine-design-"https:"-"https:"-"https:"-"https:" PhD positions in Germany
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. Background information on all DC positions is available on https://www.microman4health.eu/?page_id=74 . MicroMan4Health is funded by the European Union's Horizon Europe research and innovation programme under
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on futuristic technologies in the field of machine learning and computer vision. Hence, we investigate and develop an innovative computation-in-memory (CIM) solution for artificial intelligence accelerator design
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. Your job: You will design, build and experimentally investigate a water-based electric propulsion system. Your work will focus on propellant management, plasma generation and system performance under
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, investigates how smart and autonomous technologies can support social encounters in public space. It brings together Human-Computer Interaction (HCI), Design Research, and artistic research through speculative
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University Göttingen (Germany) Project Title: Structure-based design of novel peptide-derived β-catenin inhibitors Objectives: 1. Identify of hot spots in peptide binders. 2. Constrain the bioactive
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multiple technological platforms - photonics, electronics, biological neurons. Responsibilities and tasks This PhD project aims to (1) optimize the design of hybrid electrical–optical computing architectures
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numerical simulations and machine learning in order to better understand and characterize active matter systems. A possible direction is to use physics-informed machine learning techniques to connect
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institute website: https://www.fz-juelich.de/en/pgi/pgi-3 and the research group in which this position is located: https://www.fz-juelich.de/en/pgi/pgi-3/groups/multitip We work on the very latest issues
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Max Planck Institute of Colloids and Interfaces | Potsdam, Brandenburg | Germany | about 2 months ago
) for Functional Material Design in Summer 2027! We are recruiting doctoral candidates to help shape a resource-efficient future. The candidates will work on individual projects exploring how we can learn from
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The successful candidate will develop generative machine-learning methods for amorphous molecular thin films — the supramolecular structures that govern the performance of organic-electronic materials