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Field
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-driven proteomic. Who are we looking for? A motivated researcher with strong programming skills and knowledge of machine learning algorithms, bioinformatics, proteomics, and molecular biology, keen to
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analysis, AI algorithm modeling, testing, and integration into functional systems within the project scope. Specifically, in activities related to behavior modeling from IoT device data, generative AI
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position (contract-based) and one PhD fellowship in Computational Biophysics/Chemistry (see also https://constructor.university/comp_phys). The PhD position is focused on efficient algorithms
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modes (e.g., HCCI) for net-zero fuels like hydrogen and ammonia. A key innovative pillar is the development of an AI-driven control strategy. Machine learning algorithms, including reinforcement learning
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interference, while ensuring energy-efficient and scalable operation. This PhD project will focus on developing machine learning algorithms to enable robust channel estimation, intelligent user association
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of an image analysis algorithm for particle tracking and speed quantification. Requirements for candidates: Essential: BSc and MSc in biochemistry, biology, biophysics, biotechnology, biomedical engineering
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algorithms to extract clinical indices and provide new digital biomarkers for sleep medicine. The aim of the project is to develop new algorithms and tools of Digital Health for non-invasive, home-based sleep
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spanning design, modelling and simulation of photonic systems, sensor systems, signal processing and device manufacturing, development of machine learning algorithms, and design of optical communication
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. Experience working on inversion problems (e.g., MCMC type algorithms) Proficient in English. For information please check the Graduate Schools Admission Requirements. Familiarity or interested in using machine
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vision to reduce algorithmic complexity by orders of magnitude, e.g. by tracing paths of trees and extraction from knowledge bases (KBs), as opposed to pure DL Defining specific CSK-premises (in