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Technologies. Over the past decade, our group has successfully used a steerable tweezer platform as an optical collider to investigate cold collisions between ultracold atomic clouds. In parallel, we have built
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training and inference of GMMs for large, high-dimensional datasets Explore parallelization strategies to leverage modern GPU architectures Benchmark GPU-based implementations against CPU-based approaches
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tested by isothermal titration calorimetry (ITC) or microscale thermophoresis (MST) in collaboration with the lab of Prof. Andy Lovering. In parallel, minibinder/effector pairs will be co-expressed using
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algorithm development and application. Familiarity with common scientific programming languages such as C++. Experience in parallel programming with one or more common parallel programming models, like MPI
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other research services and resources. Strong programming skills in languages such as C, C++, Fortran, Python, or other languages commonly used in HPC environments. Deep understanding of parallel
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turbulence modelling, CFD mesh generation and use of parallel computing Have relevant experience in working with aerosols and droplets Proficient in handling large data sets and the ability to analysis and
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- and neuron-targeting anti-obesity drugs in these ex vivo systems. In parallel, you will collaborate with a team of postdocs, PhD students, and technicians working with genetic animal models
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Requirements: The candidate must be able to demonstrate a deep knowledge of supercomputing software development for massively-parallel computer architectures, and expertise in working with large-scale software
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and optimization strategies for large-scale or streaming data. Develop parallelized and GPU-accelerated learning modules, ensuring scalability and performance efficiency. Build and maintain robust data
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, and parallel computing, with a proven ability to work within highly secure and regulated environments. This role involves close collaboration with security teams, scientists, and IT leadership to ensure