81 algorithm-development-"St"-"St" PhD positions at Forschungszentrum Jülich in Germany
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numerical modeling and validation of brain-inspired algorithms Develop circuit-plausible training and inference algorithms, and analyze their behavior in LTspice and Cadence Spectre Perform algorithm–circuit
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Your Job: The conventional, manual co-design of algorithms and hardware is slow and inefficient. Our group develops methods and tools to automate the co-design process. The core of this project is
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efficiency. Your Job: Develop and apply meta-optimization that can automatically search for the best algorithm-hardware pair Tackle the challenge of computationally expensive meta-optimization procedures by
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Infrastructure? No Offer Description Work group: IBG-5 - Computergestützte Metagenomik Area of research: PHD Thesis Job description: Your Job: Development of automated workflows (Galaxy, CWL, or Nextflow) for QC
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Your Job: Development of automated workflows (Galaxy, CWL, or Nextflow) for QC (mzQC) and high-throughput analysis (identification, relative & absolute quantification) for lipidomics Implementation
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in the ChemPRINT project. Research in the Sustainable Photovoltaics group focuses on the development of solar cells and modules with improved recyclability. Further information can be found at https
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experimental systems for cryogenic measurements Development of a microwave quantum control & readout stack Development of Python code to operate quantum systems Detailed experimental characterization
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devices. Your tasks in detail are: Design and fabrication of superconducting quantum circuits Setting up experimental systems for cryogenic measurements Development of a microwave quantum control & readout
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to co-design algorithms and circuits to develop efficient neuromorphic hardware, tailored to target tasks. In detail, you will: develop circuit-plausible training/inference algorithms and analyze in
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Your Job: The accelerated development of advanced materials is essential for addressing major challenges in energy, mobility, and sustainability. Traditional trial-and-error methods in materials