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        1. 您的位置:中國博士人才網(wǎng) > 博士后招收 > 海外博士后招收 > 美國阿貢國家實(shí)驗(yàn)室納米材料中心2023年招聘博士后職位

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          美國阿貢國家實(shí)驗(yàn)室納米材料中心2023年招聘博士后職位

          時(shí)間:2023-09-15來源:中國博士人才網(wǎng) 作者:佚名

          美國阿貢國家實(shí)驗(yàn)室納米材料中心2023年招聘博士后職位

          美國阿貢國家實(shí)驗(yàn)室(Argonne National Laboratory,簡稱ANL)是美國政府最早建立的國家實(shí)驗(yàn)室,也是美國最大的科學(xué)與工程研究實(shí)驗(yàn)室之一——在美國中西部為最大。阿貢前身是芝加哥大學(xué)的冶金實(shí)驗(yàn)室 (Metallurgical Lab),現(xiàn)在隸屬于美國能源部和芝加哥大學(xué)。諾貝爾物理學(xué)獎(jiǎng)得主費(fèi)米于1942年在此領(lǐng)導(dǎo)小組建立了人類第一臺(tái)可控核反應(yīng)堆(芝加哥一號堆,Chicago Pile-1),完成了曼哈頓計(jì)劃的重要一環(huán),并且使人類從此邁入原子能時(shí)代。

          Postdoctoral Appointee - NST -Center for Nanoscale Materials

          Argonne National Laboratory

          Job Description

          The Center for Nanoscale Materials (https://www.anl.gov/cnm) at Argonne National Laboratory (Chicago, US) has an immediate opening for a Postdoctoral Appointee in the use of computational modeling and machine learning to interpret experimental x-ray, electron, and scanning probe characterization data, especially for energy materials. The postdoctoral researchers will work within the group of Dr Maria Chan (https://www.anl.gov/profile/maria-k-chan) on developing and applying algorithms and software for the use of first principles and atomistic modeling, together with machine learning, to accelerate the inversion of experimental characterization data, especially x-ray spectroscopies.

          Position Requirements

          · Recently received or near completion of their Ph.D. in Nanoscience or related discipline within the last 3 years.

          · Density functional theory modeling of materials, e.g. high throughput calculations, structure prediction, classical and ab initio MD simulations

          · Simulation of experimental characterization signals, e.g. x-ray spectroscopy, electron microscopy, scanning tunneling microscopy, and neutron scattering

          · Use of machine learning approaches

          · Development and implementation of algorithms and software for materials modeling

          · High-performance and parallel computing

          · Excellent communication and analytical skills

          · Ability to work independently and in an interdisciplinary collaborative environment, in close collaboration with experimentalists.

          · Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork.

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