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Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to biological data collection, extraction, and standardization
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-Chem • You will be contributing to the development of machine learning models used on data from Poleno Jupiters, applying Python and machine learning. • The position will focus on implementing
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significant contributions to fundamental machine learning research, possessing a combination of mathematical maturity and advanced engineering skills: Education: A PhD in Computer Science, Mathematics
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statistics, AI and machine learning methods, including demonstrated experience in analysing multiple global change drivers, e.g. land use intensity, climate change, nitrogen deposition. Proven capability
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/augmented/extended (VR/AR/XR) environments to support learning of scientific concepts and practices at the university-level. The main aim of this work package is to investigate how such cutting-edge
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circuit models and algorithms for estimating the charge, health, and power based on direct methods (e.g. open circuit voltage), model-based methods (e.g. Kalman filtering), data driven methods (e.g. machine
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framework for understanding emergent deception in human-AI interaction by uniting behavioural-psychological, economic-strategic, and machine learning perspectives. The postdoc will be jointly supervised by
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by the Carlsberg Foundation. SMARTbiomed is a research center with core mission to develop statistical and computational methods focusing on causal inference, risk prediction and machine learning
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Are you an experienced researcher in microbial genomics and bioinformatics with a strong record of university teaching, and expertise in whole-genome sequencing (WGS) analysis, machine learning and
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. Qualifications Applicants at Postdoctoral Researcher level should hold a PhD in AI enabled learning, educational technology, information systems, computer supported learning, social entrepreneurship, innovation