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for time-series/wearables data, demonstrated by relevant courses and master thesis. Having publications in the area is highly valued as well demonstrated ability to work both independently and as a team
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First Stage Researcher (R1) Country Netherlands Application Deadline 18 Jan 2026 - 22:59 (UTC) Type of Contract Temporary Job Status Not Applicable Hours Per Week 38.0 Is the job funded through the EU
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towards a new conceptual model that explains the individual dynamics of fatigue. The project will start with an analysis of already-collected data. You will use several forms of time series analysis
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experience with deep learning, machine learning and/or time series analysis. Good programming skills in Python or similar languages. Experience with using machine learning in the context of neuroscience
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data, including regression, classification, representation learning (e.g., spectra, micrographs, or time-series), and uncertainty-aware modelling for experiment planning. You are proficient in Python and
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European consortium, we bring together industry partners, municipalities, academia, learned societies, and patient organisations to cocreate, pilot, evaluate, and iterate a series of city-level strategies
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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
for rapid and resilient decarbonization. Your research will combine time-series analysis, supervised and unsupervised learning, and explainable AI methods to uncover the dynamic patterns preceding
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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
resilient decarbonization. Your research will combine time-series analysis, supervised and unsupervised learning, and explainable AI methods to uncover the dynamic patterns preceding technological
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studying k-space (Fourier domain of the image in which the acquisition is performed) samples from over the entire time series, a neural-implicit representation can infer what the full k-space should look
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of finance, and be undertaken using appropriate methodology. Typically, students work on their first project together with their advisor(s), and gradually become more independent over time. At least one of the