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Field
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diversity methods, data management, reproducible code, R/Tidyverse, machine learning and AI for ecologists, visualisation, evidence-based policymaking, science-policy communication, and policy brief writing
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or heterogeneous environmental datasets Familiarity with spatial analysis, GIS, or geospatial data workflows. Experience with machine learning, modelling, or systems analysis approaches Interest in resilience
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, MATLAB, C/C++, etc.) and/or knowledge of holographic imaging is a plus; Prior knowledge and hands-on experience with state-of-the-art machine learning frameworks (e.g., sci-kit-learn, Tensorflow, PyTorch
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design and characterise reconstruction methods—both machine-learning-based and traditional—for the bundles of muons that reach the detectors, and apply them to data and simulations to constrain cosmic-ray
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Description PhD Positions in Neurotechnology: Next Generation Non-invasive Neurotechnologies & Brain Therapies The Neurotechnology Group of Prof. Mehmet Fatih Yanik at ETH Zurich is recruiting outstanding PhD
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nanocrystals, hybrid perovskites and 2D materials. Development of new data-driven approaches for studies of optoelectronic properties using EM, including machine learning / machine vision algorithms. The balance
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. The supervision team includes: Prof. Ivan Depina – main supervisor and coordinator, probabilistic modelling, scientific machine learning Prof. Mohamed Hamdy – building performance simulation, building automation
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. • be located at the agreed project location(s) and, if required, comply with the university’s external enrolment procedures. Selection criteria Skillset: Proficient in Python, machine learning, and
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actively on the preparation and defence of a PhD thesis in the field of explainable reinforcement learning (XRL). Explainable reinforcement learning aims to make decisions, policies, and learning processes
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actively on the preparation and defence of a PhD thesis in the field of continual reinforcement learning. Continual reinforcement learning studies how agents can learn across a sequence of changing tasks