45 computer-programmer-"UCL"-"UCL" Postdoctoral positions at Duke University in United States
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, neuroscience, physiology, physics, or computer science · Be a proficient programmer and experience in Python, MATLAB, NEURON, COMSOL, and / or git are assets · Be familiar with neural biophysics and
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or scholarship, and may include teaching responsibilities. The appointment is generally preparatory for a full time academic or research career. The appointment is not part of a clinical training program, unless
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methods like umbrella sampling, force integration, free energy perturbation, or lambda integration to compute the conformational behavior of individual structures and the thermodynamic stability of assembly
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, Duke University Biology Department to study how archaeal microbial communities respond to stress in hypersaline environments. A PhD in computational and/or experimental biology is required in fields
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collaboration with Dr. Suthana and interdisciplinary team members. · Apply advanced statistical and computational approaches to investigate neural dynamics underlying memory consolidation and navigation
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develop novel computational approaches. Develop mathematical descriptions for the acquired data and work with our theorists collaborators to implement new theories. Integrate with the rest of the lab and
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conventional methods like nonlinear FEM, and comparing the results to computational observations. 3) Support the educational activities of the Pl through graduate student mentoring, selected lectures, and
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are central to the lab’s focus on enabling high-throughput, programmable experimentation in synthetic and regenerative biology. The candidate brings specialized expertise in stem cell biology and bioengineering
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mentoring, if needed. Minimum Qualifications The candidate should have a Ph.D. in Engineering, Applied Mathematics, Computer Science, or a related area. Experience 0+ years of postgraduate experience. Skills
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, evolutionary biology, computer science, physics, applied mathematics, or engineering. Our research integrates mathematical modeling, machine learning, and quantitative experiments to understand and control