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Assistant Professor in Marine Biology & Ecology - Biomedical Science or Quantitative Systems Ecology
ecologist working in coastal systems, who applies modern approaches in causal inference, experimental ecology, spatial modelling, and data science, including the use of machine learning to produce rigorous
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Systems at The Technical Faculty of IT and Design invites applications for PhD stipends or integrated stipends in the field of Machine Learning for Intelligent Hearing Assistance in Complex Acoustic
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robust data pipelines, creating efficient machine learning models, and integrating AI capabilities into existing systems to improve efficiency, accuracy, and service quality while reducing operational
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/machine learning to expand the interdisciplinary faculty cluster on Translational Predictive Biology (CTPB). The selected faculty is expected to synergize with existing Translational Predictive Biology
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, single-unit recordings. 2. Strong background in computation modelling of behavioral or neural data. 3. Proven experience with statistics, machine learning and/or brain stimulation. 4. Proven experience
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, test and measurement methodologies for electronic modules, system engineering, data pre-processing and database indexing/analytics for dashboarding/visualisation, embedding machine learning algorithms
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for this position, the following is required: PhD in data or computer science, machine learning, AI, statistics, mathematics, biophysics, bioinformatics. Additional requirements In addition to your CV and your
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extraction. 2. Be responsible for the application of AI and machine learning techniques to improve tissue image interpretation, for use in case selection and tissue annotation for tissue microarray
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. You will contribute to developing datasets, baseline models, personalized learning engines, reasoning-graph representations, cross-domain mapping algorithms, and RLHF-style feedback loops that improve
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publications or conference presentations (either as first-author or as a co-author) Machine learning and/or computational modelling experience Experience with brain network modelling and analysis Experience