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(Kingdom of the) [map ] Subject Areas: Stochastics, artificial intelligence and machine learning Appl Deadline: 2025/06/02 11:59PM (posted 2025/04/02, listed until 2025/06/03) Position Description: Apply
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of Amsterdam. Interested in developing fundamental machine learning techniques for tabular data to democratize insights from high-value structured data? Then this fully-funded 4-year PhD position starting Fall
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, machine learning and biophysics. Investigators at CFIN are supported by state-of-the-art research facilities, including MRI, MEG, OPM-MEG, EEG, PET, TMS, eye-tracking and more. CFIN is part of the Danish
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synthetic fuel reactors. Tasks include gas handling, system diagnostics, thermal integration, and performance evaluation under variable power inputs. Data Analysis and Machine Learning: Collect and process
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Machine Learning Problems > Constantly questions finance/trading data and stays motivated to seek answers despite most often proving that there is no correlation or signal > Experience in setup of research
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Machine Learning Problems > Constantly questions finance/trading data and stays motivated to seek answers despite most often proving that there is no correlation or signal > Experience in setup of research
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Sciences division. This multidisciplinary team utilises a combination of machine learning and mechanistic modelling to derive models and scientific insights from data, which both support and enhance drug
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Project 2: Experience working with large datasets and machine learning Reasonable proficiency in any coding language used in data science Mixed-methods research experience Online Application Required
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observations. Your major challenge is in model development, and there is room for you to develop machine learning applications in the field of firn modelling. If successful, your work will lay the foundation
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, Amsterdam and Freiburg, will analyse the impact of blockades on households, states, corporations and the international order; on the development of political and military strategy; on how the wars were