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subpopulations, as well as (plastic) cancer cell states that contribute to tumor progression, metastasis and therapy resistance. The candidate will lead several projects applying machine learning to (single-cell
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chains. Has demonstrated experience analyzing textual data using NLP or other machine learning techniques. Has excellent English-language academic communication skills (both written and oral) – CEFR level
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competencies Education You should have completed within the past five years or be close to completing a PhD in a relevant field such as data science, AI, computer science, machine learning, Earth system science
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of machine learning (ML) and quantum many-body physics. We are also happy to work with experts in one of the two fields who are committed to learning the other. Moreover, we look for interest in developing
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(PHM) for aerospace operations and maintenance, with strong expertise in sensor fusion, stochastic modeling, and machine learning. Your role: Design, build, and commission a laboratory test bench for
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natural selection acting on genes, developmental experiences, and learning in context. To illustrate, animals might exhibit anxiety in the absence of immediate threat when encounters with those threats (e.g
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-based and machine-learning approaches), the digital twin will provide decision-makers and industry stakeholders with actionable insights about when, where, and how corrosion risk evolves. As a postdoc
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construction, and a robust foundation in statistical spectral analysis, including familiarity with (or strong interest in) chemometrics and/or machine learning algorithms. Job requirements The Ideal Candidate
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24 Oct 2025 Job Information Organisation/Company Delft University of Technology (TU Delft) Research Field Computer science » Computer hardware Computer science » Digital systems Engineering
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electron ground states. Another promising route towards physical implementations of energy-based machine learning and neuromorphic hardware is to utilise material platforms that exhibit multiwell behaviour