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materials, sensors/biosensors, polymer science, and micro-/nanofabrication techniques is desirable Very good written and spoken English skills Our offer A vibrant research community in an open, diverse and
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: Development of machine learning algorithms for the localisation of seismic sources (e.g., on 2D grid maps) Analysis and preprocessing of large DAS datasets Use of synthetic training data from seismic
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simulations for plume-in-grid models Requirements: university degree (MSc or equivalent) in the field of aerospace engineering, physics or similar solid knowledge of Physical and Analytical Chemistry (phase
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(varying grinding and flotation conditions); collect and curate high‑frequency time‑series data Train particle‑based separation models (PSMs), linking micro‑structural descriptors to flotation performance
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sets and health economic modelling are desirable. The candidate should have experience and/or familiarity with quantitative methods used in micro-econometrics and/or biostatistics. In addition