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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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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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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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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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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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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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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
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. The project will optimize fishing practices to reduce ecological impacts, providing actionable insights to inform policy decisions that balance economic performance with environmental conservation. Project
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accepted all year round Details Model predictive control (MPC) is a popular advanced control technique that solves a constrained optimal control problem, on-line, at each sampling instant. The first control