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Field
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will be tailored to your expertise, spanning from hardware design to system-level optimization and control methods. For the AI position, you will develop machine learning models that incorporate physical
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, and conceptual domains of behavior analysis. The program is accredited by the Association for Behavior Analysis International and follows a junior colleague model with close supervision and mentoring
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systems (ITS). In particular, the successful candidate will conduct cutting-edge research in: Developing physics-informed neural networks (PINNs) for complex dynamical systems modeling and observer design
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pathways driving leukemic transformation during haematopoietic development. The successful candidate will use in vitro differentiation systems (ESC/iPSC) to model normal and malignant haematopoiesis
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modeling in smart mobility systems Required Qualifications: The requirements for the applicants include: Ph.D. in Electrical Engineering, Computer Science, Operations Research, Applied Mathematics
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molecular biological and quantitative techniques including microscopy, flow cytometry, computational/mathematical modeling, and utilizes approaches from synthetic biology and complex biological systems
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academic and professional qualifications Proven research experience in the field of modelling and analysis of biological networks Solid foundation in mathematics and algorithmic design Strong programming
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
The Mathematics and Computer Science (MCS) Division at Argonne National Laboratory invites outstanding candidates to apply for a postdoctoral position in the area of uncertainty quantification and
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systems. Responsibilities and qualifications You will be responsible for mathematical modelling of dyadic and group interaction data (e.g., using coupled oscillator models), working with movement
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Pneumatic Tires, Structure-Process-Properties Relationships. How will you contribute? Do you have proven skills in data analysis, machine learning, as well as in mathematical and computational modelling? You