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We are looking for a strong, motivated PhD-student with an analytical, algorithmic background who is interested in pursuing a PhD-project devoted to the allocation of donor organs to patients. The goal
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to ensure high predictive capability and, on the other hand, keeping the models sufficiently “compact” (i.e. algorithmically small and computationally efficient) to enable incorporation in integrated PED
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control algorithms lies a physics-based simulation model, whose accuracy largely determines the effectiveness of the control loop. Position 3 – High-fidelity simulation of the LAFP process Current
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cryptography guarantees better performance and faster speed for encrypting data. Without doubt, AES (Advanced Encryption Standard) and Keccak/SHA-3 (Secure Hash Algorithm 3) are the two most used and famous
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advantages for manipulation and locomotion, but current control algorithms do not fully exploit their capabilities. Most rely on approximations tailored for rigid systems or require extensive sensing and
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trading decisions under high price volatility. This PhD position focuses on designing, developing, and evaluating self-learning energy trading algorithms that are able to cope with these challenges. By
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on the development, optimization, and clinical evaluation of new x-ray-based imaging methods. The lab focuses on the use of medical physics approaches to improve image acquisition methods and processing algorithms
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operate safely around humans. They offer unique advantages for manipulation and locomotion, but current control algorithms do not fully exploit their capabilities. Most rely on approximations tailored
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to make viable trading decisions under high price volatility. This PhD position focuses on designing, developing, and evaluating self-learning energy trading algorithms that are able to cope with
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Europe, Altair, MIT and 12 other partners to build an AI Partner in Engineering (AIPE) that converses with engineers, proposes designs, explains its reasoning and even writes new optimisation algorithms