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or incomplete. Information Your tasks will include: Developing and benchmarking ML/AI algorithms tailored to low-data regimes — e.g. few-shot learning, transfer learning or data-efficient representation learning
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theory to unlock the efficiency of neuromorphic compute in multi-timescale processing. Job description Neuromorphic computing promises new energy efficiency records by taking inspiration from biology
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18 Oct 2025 Job Information Organisation/Company Delft University of Technology (TU Delft) Research Field Biological sciences » Biology Computer science » Informatics Computer science » Programming
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-on monitoring with cutting edge data-driven and physical based models, including the deployment of machine learning algorithms. The project aims to have a tangible impact on the way urban waters are monitored
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will work in an interdisciplinary environment at the TU/e Laboratory of Chemical Biology and the Expertise Center Clinical Chemistry Eindhoven (ECCCE), a collaboration between TU/e, Catharina Hospital
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for broader clinical implementation of tumor marker assays and more reliable, minimally invasive diagnostics. You will work in an interdisciplinary environment at the TU/e Laboratory of Chemical Biology and the
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optimizations tailored to different environments. The optimizations range from algebraic optimizations (e.g., term rewriting) to algorithmic optimizations (e.g., group level algorithms), and to hardware
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., term rewriting) to algorithmic optimizations (e.g., group level algorithms), and to hardware optimizations (e.g., automated pipelining). The PhD student will be supervised by Nusa Zidaric. Key
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., term rewriting) to algorithmic optimizations (e.g., group level algorithms), and to hardware optimizations (e.g., automated pipelining). The PhD student will be supervised by Nusa Zidaric. Key
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limitations. The field of interpretable machine learning aims to fill this gap by developing interpretable models and algorithms for learning from data. Meanwhile, the field of knowledge discovery and data