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Details Title Postdoctoral Fellow in Riemannian Optimization School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Position Description A postdoctoral position is
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modular forms, particularly work with Klein forms and congruences satisfied by Fourier coefficients. Knowledge of special functions and q-series. Ability to deduce combinatorial information from
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perturbation screens with high content molecular and imaging data to understand cellular and multi cellular combinatorial programs in cells and tissues in health and disease. You will join a highly collaborative
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other associates to develop optimized photonic test structures that will be incorporated in PIC process flows and enable extraction of key physical parameters associated with PIC performance, including
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-affiliated hospitals. Responsibilities Design and optimize new CAR constructs and delivery systems Conduct in vitro assays to evaluate cytotoxicity, cytokine production, and phenotype Perform in vivo efficacy
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; analyze and interpret data Apply data to the iterative design of optimized chemical probes suitable for cellular and in vivo pharmacological studies Maintain accurate lab records and documentation
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and analytical thinking for research challenges. Flexibility in adjusting research direction as needed. Knowledge of energy storage systems and electric drives Coding experience. Math and optimization
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. Research areas include Representation Learning, Machine learning and Optimization on graphs and manifolds, as well as applications of geometric methods in the Sciences. This is a one-year position with
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stabilizers and targeting motifs for delivery and persistence of activity in the brain-tumor microenvironment. · Formulate and optimize liposomes and polymeric/hybrid nanoparticles for delivery across
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synthetic chemistry and reaction optimization. A working knowledge of molecular biology and/or protein generation. For information about the (lab) at Genentech and publications, please go to: https