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Sensor Fusion: Strong background in processing and fusing data from cameras, LiDAR, radar, or other sensors for robust autonomous system perception ML Engineering and Experimentation: Proficiency with
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. Approval of remote and hybrid work is not guaranteed regardless of work location. For additional information on remote work at Penn State, see Notice to Out of State Applicants . This is a term position
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the online experimental data 20% - Develop and implement field experiments 15% - Manage and statistically analyze the field experimental data 15% - Design and conduct interviews with HVAC technicians 10
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/FSI) of cardiac valves. * Integrate experimental data with simulation results to evaluate stress and strain distributions. * Collaborate with clinicians, engineers, and scientists across disciplines
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Fritz Haber Institute of the Max Planck Society, Berlin | Berlin, Berlin | Germany | about 2 hours ago
in close cooperation with theory. An interest in modern methods of data analysis is therefore highly welcome. The required qualifications include a PhD in Chemistry, Physics or Materials Science, as
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Phenomic prediction programs that incorporates genotype (genotype by sequence) and phenotype data (from traditional and high throughput, remote sensing sources) -Use or develop R programming code to complete
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, exercise physiology, health communications, public health, social sciences, life sciences, engineering, data and computation science, and economics. Healthy Living implements, evaluates, and disseminates
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service, or genetic information in its administration of educational policies, programs, or activities; admissions policies; scholarship and loan programs; athletic or other University-administered programs
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Description Job Responsibilities: 50% - Utilize graphical/network models and data integrations, including work on collaborative projects. 10% - Assist in maintaining and upgrading, as necessary, our server
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: Computational materials modeling: DFT, molecular dynamics, phase-field modeling, or multiscale simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis