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-in-time simulations to explore how various parameters contribute to extinction outcomes. These results will then be used to train machine learning models capable of predicting extinction probabilities
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research, publication, data analysis, and laboratory work, with the opportunity to participate in planned field expeditions to Greenlandic coastal and shelf waters. You will investigate key parameters
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. Your primary tasks will be: to print 316L and 17-4 PH stainless steel samples with optimized parameters to optimize the aging treatment of AM 17-4 PH stainless steel to perform non-destructive 3D and 4D
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computer architecture. Responsibilities and qualifications You are expected to conduct independent research in collaboration with and under the guidance of experienced colleagues. Additionally, you will be
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internationally recognised academic environment with over 400 employees and 10 research sections. We broadly cover digital technologies within mathematics, data science, computer science, and computer engineering
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academic environment with over 400 employees and 10 research sections. We broadly cover digital technologies within mathematics, data science, computer science, and computer engineering, including artificial
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tasks will primarily encompass: Simulation of heat transfer and optimization of façade-TEG configurations. Characterization of TEG generators after accelerated fatigue testing to estimate their long-term
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Job Description These days, the inner workings of molecules and materials can be probed and modelled by advanced simulation tools on modern computer architectures. However, the routine applications
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or related fields. Preferably strong interest and foundation in immunology, immune cell assays, or translational research. Preferably experience with computer-guided protein engineering. Experience in human
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: Review and synthesize epidemiological evidence to estimate health impacts of foods, considering both risks (chemical, microbiological, nutritional) and benefits (nutritional). Develop dietary exposure