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Field
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to predict and design microbial community dynamics and functions Apply active learning frameworks to optimize metabolic activities of microbial communities in silico Required Qualifications at this Level BS in
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. We are playing a decisive role in shaping far-reaching processes of global change—from energy transition to artificial intelligence—through outstanding scientific knowledge and innovative academic
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; Continuum Mechanics; Geometry and Mathematical Physics; Mathematical Biology and Healthcare; Mathematical Data Science; Nonlinear Systems; Numerical Analysis; Optimization; Quantitative Methods for Finance
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nonlinear, including neural networks), loss functions (squared error, cross entropy, hinge, exponential), bias and variance trade-off, ensemble methods (bagging and boosting), optimization techniques in ML
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computational methods, that leverage high-performance computing power, to develop advanced tools. The successful candidate will be expected to develop machine learning methods that integrate physical
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a functional. A key challenge lies in determining the regularity of solutions relative to parameters. For practical applications, choosing numerical methods with optimal convergence rates should align
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intern in multiagent trajectory optimization. The successful candidate will help develop and implement mixed-integer and nonlinear programming methods for coordinating multiagent UAV systems. This role
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direct access to state-of-the-art labs, cutting-edge infrastructure, and specialized development and product teams tailored to your project’s unique needs—facilitating rapid iteration and optimized drug
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positioned team of experts in the field of photonic components, laser sources, and nonlinear converters. You will contribute to the improvement of fiber components and new designs. You will independently work
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(e.g. first-order optimality, gradient descent algorithms, and basic linear or nonlinear programming) is required. Object-oriented programming experience is required. Experience in parallelizing code is