6 machine-learning-phd "The University of Queensland" scholarships

PhD : Understanding and predicting seagrass decline in lagoonal environment with a modelling approach (M/F)

and biology Good knowledge of machine learning methods (e.g. Bayesian networks or Machine Learning, neural networks) Good modeling and programming skills (e.g. Python, R, Fortran, C++). Proficiency in

PhD scholarship in digitalization of polymer-based battery electrolyte development - DTU Energy

and storage devices. We have a unique opening to expand our efforts in developing our self-driving laboratory (SDL) that combines robotics-based lab automation, machine learning, and high-throughput

PhD student in Data Driven Evolution and Biodiversity: Plant Extinction

, probabilistic modelling, machine learning and computational approaches in biodiversity science. The student will be will be supervised by Aelys Humphreys (Stockholm University) and work closely with

PhD student in Data Driven Evolution and Biodiversity: Plant Extinction

, probabilistic modelling, machine learning and computational approaches in biodiversity science. The student will be will be supervised by Aelys Humphreys (Stockholm University) and work closely with

PhD student in Data Driven Evolution and Biodiversity: Plant Extinction (DDLS Research School)

modelling, machine learning and computational approaches in biodiversity science. The student will be will be supervised by Aelys Humphreys (Stockholm University) and work closely with an international team

SmartSat CRC and SA Water PhD Scholarship - Advancing the monitoring of seagrass dynamics with hyperspectral remote sensing

the University of Queensland and CSIRO, supported by the South Australian Water Corporation and the Department for Environment and Water, and managed by the SmartSat CRC. Research objectives: The overall objective