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positively impact the world. Like the city we call home, UB is distinguished by a culture of resilient optimism, resourceful thinking and pragmatic dreaming that enables us to reach others every day. Visit our
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the emphasis of the position will be on the development of nanomaterials for AM and understanding of AM process optimization, functional materials design and compositional grading, electrochemical and
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(cholesterol esters) and proteins; in particular, the person recruited will be responsible for optimizing the parameters for neutral lipids. • Develop and implement new methodologies for analyzing molecular
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. The candidate should have a strong background in process modeling, control, optimization, applied machine learning, and AI. City: Cranbury State: NJ Location: Off Campus Create a Job Match for Similar Jobs About
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, biologists, and data scientists. The emphasis will be on enabling high-fidelity image reconstructions from sparse and noisy data, leveraging state-of-the-art methods in compressed sensing, optimization, and
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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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to provide the highest level of care for all children with cancer, with optimal quality of life. The center brings together the best possible care and scientific research, creating a unique interdisciplinary
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. Experience working with rodent models. Experience with mammalian cell culture. Experience with live-cell or in vivo microscopy. The optimal candidate will have a background in neuroscience or cell biology, as
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, Quantum Biomedicine, Quantum Machine Learning, Quantum Optimization, etc. The positions are normally for one year and are renewable for a second and a third year, subjected to satisfactory research
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” focusing on the effect of a fluctuating environment on the collective dynamics of self-propelled agents, a numerical part on “reinforcement learning” focusing on optimizing communication between agents in a