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Senior Researcher in Synthetic Biology and Metabolic Engineering of power-to-X utilizing Microorg...
. We are seeking a Senior Researcher to lead pioneering work in synthetic biology and metabolic engineering, with a particular focus on non-model prokaryotic microorganisms capable of utilizing power
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, preferably Reinforcement Learning (e.g., Q-learning, Deep Q-Networks) or other control algorithms. Proficiency in Python, MATLAB, or similar for data analysis, modeling, or AI implementation. Strong written
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microbial metabolites and its effect on chronic kidney disease and cardiovascular complications, using an in vivo model of chronic kidney disease. Responsibilities and qualifications As a PhD student, you
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and Northeastern University, USA. Responsibilities The PhD project involves developing a flexible vegetation model within the OpenFOAM platform, where vegetation stems are represented as nonlinear
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mathematical foundation of machine learning models. You will be responsible for developing scientific machine learning methodologies enabling new approaches for solving machine learning problems including
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Foundation Models initiative . The proposed starting date is 1 September 2025 or soon thereafter. The appointment will be made for a term of three years at a competitive salary and will follow the PhD study
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with the aim of making it a practical computational tool in the near term. The project will focus on Gaussian Boson Sampling (GBS), which is a specialized photonic quantum computing model with
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the Computer Science study program. The stipend is open for appointment from August 1st 2025 or soon thereafter. The PhD students will be working on topics within the general areas of formal methods, model checking and
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recognised business school with deep roots in the Nordic socio-economic model. Our faculty has a broad focus on societal challenges, and we have earned a reputation for high-quality disciplinary and
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-cutting and bending to break the glass panels. The project will involve the establishment of a numerical model and the acquisition and analysis of data from physical measurements in the production