-
backgrounds, including computational chemistry, bioinformatics, systems biology, and machine learning. The project offers a unique opportunity to collaborate closely with experimental scientists and contribute
-
Travel ” which examines signal processing and machine learning methods for inferring active travel activities from optical fibre signals. About You Applicants must have an Undergraduate Degree in
-
first author publications in reputable peer-reviewed journals Advanced quantitative skills (e.g., advanced stats [MLM], machine learning, data mining). Willingness to develop desired skills (see directly
-
research into planet formation/protoplanetary discs or the ISM/star formation and may also have some experience in statistical methods and/or machine learning. Dr Winter and QMUL are committed to improving
-
to science. This is the first large-scale study of its kind, and your results will establish a legacy of scientists working with funding councils to defend their research. Cutting-edge machine learning
-
bioinformatic workflows. Familiarity with biomedical ontologies and text mining on Electronic Health Records and biomedical literature Knowledge of machine learning / deep learning with an interest in
-
the areas: AI, deep neural networks, machine learning, applied topology, probability, statistics, signal processing. About the School The School has an exceptionally strong research presence across
-
-quality robotics research in the areas of robot grasping and manipulation, kinematics and mechanisms, sensing, and human-robot interaction. Within CORE, SAIR focuses on multimodal machine learning for human
-
area, with content covering robotics and machine learning, and excellent programming skills in Python. You should have research experience in either robotics or machine learning. You should also have
-
-quality robotics research in the areas of robot grasping and manipulation, kinematics and mechanisms, sensing, and human-robot interaction. Within CORE, SAIR focuses on multimodal machine learning for human