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research project (e.g. PhD in physical chemistry, chemical physics, computational chemistry or relevant) and needs to be passionate about research. Experience in usage Gaussian, ADF, Dalton, ORCA etc
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for an accurate simulation of time-dependent flows, enabling sensitive applications such as aeroacoustics. Furthermore, the high scalability on massively parallel computers can lead to advantageous turn-around
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, and measure success. Basic Qualifications: A PhD in Theoretical Physics or a related discipline completed within the last 5 years. Experience with High Performance Computing and programming
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dynamics (DNS, LES, or RANS) and/or high-performance computing (MPI, GPU, or parallel solvers), as demonstrated by application materials. Evidence of peer-reviewed publications in fluid dynamics, turbulence
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computational fluid dynamics (DNS, LES, or RANS) and/or high-performance computing (MPI, GPU, or parallel solvers), as demonstrated by application materials. Evidence of peer-reviewed publications in fluid
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– in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in materials science, applied mathematics, computer science, or an AI related field completed within
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multiphase flow in porous media. 80% - Applying numerical and analytical infiltration models to quantify groundwater recharge potential under varying hydrogeologic conditions. In parallel, the researcher will
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teaching load of 2 classes per year. The position is for two years, and can be renewed for a third year pending satisfactory performance. Required Qualifications ● A PhD in Mathematics or an equivalent area
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outputs are biologically and clinically meaningful. Contribute to PI-led grant applications and mentor undergraduate/graduate students. Qualifications: Required: PhD in Computer Science, AI, Data Science
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smoothly by managing reagents and supplies and performing genomic assays and assisting with long read Nanopore sequencing, functional genomics, RNA IP, RNA probe synthesis and Massively Parallel reporter