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Postdoctoral Fellow - Materials Chemistry, Texas Materials Institute, Cockrell School of Engineering
or parallel reactors Collaborate with computational scientists to integrate machine-learning models for closed-loop materials discovery Collaborate with companion postdocs on functional materials
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microscopy. This position will operate as a core part of a larger AI-robotic materials discovery program at TMI that couples liquid-phase synthesis, high-throughput sample processing, and autonomous
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single cell sequencing data and high-performance computing environment is highly preferred. A good understanding of cancer biology, immunology, cell biology, and molecular biology is also preferred. Proven
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/science/our-labs/herman-fung-lab). Qualified applicants will have experience in cell, structural or computational biology and a passion for applying and/or developing diverse tools for understanding gene
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scientific conferences and publish models and scientific insights in high-impact journals Who You Are: Ph.D. in Computational Biology, Bioinformatics, Computer Science or Machine Learning related field
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equipment, computer resources, and yearly research support. Appointments are for one year with the possibility of renewal pending satisfactory performance and continued funding. Candidates are encouraged
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growth for Portland, Maine, and northern New England. We are nurturing an environment for high-impact research and innovation in computer and data science, digital engineering, the advanced life sciences
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computational, experimental, observational, and theoretical approaches. Existing research programs at KIPAC span many areas of astrophysics and cosmology and include studies of dark energy and cosmic dynamics
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algorithms to solve hydrology and water resources problems. Familiarity with high-performance computing (HPC), cloud platforms, or GPU clusters. Demonstrated ability to work collaboratively with
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for improved interpretability and generalization. Familiarity with high-performance computing (HPC), cloud platforms, or GPU clusters. Demonstrated ability to work collaboratively in interdisciplinary and cross