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Field
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Identification of Objects in Massive, Multi-scale, and Temporal 3D Point Clouds Objectives: The MODAM3 project aims to develop new models and algorithms for the automatic localization, characterization, and
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platform that combines Building Information Modelling (BIM), Artificial Intelligence (AI), cloud computing, IoT technologies, and real-time data analytics. Working with an international consortium
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work in this area. In this role, you will have the opportunity to: Build the core decision platform at scale. Develop scalable, tested and documented local and cloud workflows for malaria peptide
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multi-omic datasets, machine learning or AI approaches, and HPC or cloud computing would be advantageous.
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, and cloud-based resources for large-scale data analysis and model development. Using these rich data resources, we develop and apply advanced AI/ML methods to model tumor evolution, identify clinically
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cloud environment such as Microsoft Azure. Experience in writing grant proposals and scientific publications. Strong communication, interpersonal, and problem-solving skills. Other Requirements
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of the molecular ISM of galaxies, with a focus on galaxy centres. In particular, this project aims to measure the spatially resolved properties of giant molecular clouds and/or weigh the supermassive black holes
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reproducible research, version control, or high-performance/cloud computing. COMPENSATION AND BENEFITS The hiring range for this position is $61,500 to $64,000. An offer of employment, if made, will be
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externally. Experience of controlled human infection or vaccination studies, integration of single-cell and multi-omic datasets, machine learning or AI approaches, and HPC or cloud computing would be
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-generation cloud chamber technology for exploring aerosol-cloud-precipitation interactions. The postdoc will lead and support cloud chamber experiments, operate a range of instrumentation, and work closely