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Field
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computational mesh generation. In this role, you will apply your software engineering skills to develop and validate computational results that support large-scale, physics-based simulations across a variety of
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. Familiarity with high-gradient MRI systems or multi-shell diffusion modeling, and hands-on experience with neuroimaging software such as FSL, ANTs, SPM, or AFNI, will also be considered advantageous. A
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epidemiology and prevention. Proficiency in statistical analysis software such as Stata, R, or SAS. Strong interpersonal and communication skills, with the ability to collaborate effectively in a team
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software such as Stata, R, or SAS. Strong interpersonal and communication skills, with the ability to collaborate effectively in a team environment. Demonstrated commitment to advancing mental health
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software programs including Microsoft Office (Word, Excel, PowerPoint) · Knowledge of computer programming/scripting (e.g., Matlab, Bash/Unix) · Knowledge and experience using image/EEG
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learning; software development Preferred Qualifications: Excellent academic writing ability on quantitative topics Familiarity with relevant software environments (e.g., R, Python, SPSS) Expertise in
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: The development of computational models to represent structural performance using commercial and research software tools The development and validation of machine learning and artificial intelligence models focused
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experience working with FPGA-based networking platforms/frameworks such as NetFPGA, Corundum, Xilinx OpenNIC, or the ETHZ HLS TCP/IP Stack. Familiarity with Software Defined Networking, SmartNICs, RDMA is an
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join the group to develop AI and machine learning based software to assist clinical workflow and pre-clinical studies. Required Qualifications: Ph.D. in a physical science or engineering field Strong
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. The job also emphasizes the dissemination of knowledge about population health, particularly through the preparation of peer-reviewed manuscripts, software packages and/or participation in international