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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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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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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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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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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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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
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models and algorithms for learning from data. Meanwhile, the field of knowledge discovery and data mining has allowed us to obtain insights from large amounts of data for decades, and it is worth