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is highly interdisciplinary, bringing together expertise from machine learning, statistics, data science, and healthcare research. The successful applicant will be expected to design and implement
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 6 hours ago
. These one- to three-year fellowships are competitive and are designed to advance NASA’s missions in space science, Earth science, aeronautics, space operations, exploration systems, and astrobiology
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of materials. The main activities will include: – adapting the generative LegoXtal code to the design of MOF coordination polymers and porous materials by assembling elementary molecular bricks in the generation
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, including training other lab members. Responsibilities Include: Research- 85% • Together with the PI, design experiments • Experimental work, including stereotaxic injections and implantations, tissue harvest
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We are seeking a highly motivated Postdoctoral Researcher to join the FNR AI-HPC 2025 BRIDGES project GenePPS, which investigates how machine learning can enable prediction of gene perturbation
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. Antonio Scialdone’s group at Helmholtz Munich, a leading European hub for AI in biology. The successful candidate will design and implement physics-informed machine learning frameworks and predictive models
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research, innovation, and education to enable a biobased society and improve human health. We explore how biological systems, and innovative technologies can be used to convert biomass into valuable products
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Responsibilities Lead independent research projects focused on EBV maintenance and oncogenesis. Design and execute mass spectrometry experiments to map viral-host protein interactomes. Validate candidate host
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to develop generative AI methods for nanoparticle drug delivery design, at the intersection of machine learning, explainability, and pharmaceutical nanotechnology. Job description We are looking for a
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design platforms for rapid deisgn and optimisation of novel targeting modules. Your responsibilities will include: Designing and implementing state-of-the-art deep learning architectures for protein