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. The researcher will work under the mentorship of Dr. Pascal Geldsetzer and receive a designation as visiting researcher at the Stanford University School of Medicine. Job-ID: V000010072 Field of application
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technologies that are vital for the future and enabling the commercial utilization of this work by business and industry, Fraunhofer plays a central role in the innovation process. As a pioneer and catalyst for
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-scale controllable, and cost-efficient disease models by bringing together experts in physical chemistry, physics, bioengineering, molecular systems engineering, machine learning, biomedicine, and disease
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areas is expected: numerical analysis, scientific computing, model reduction, uncertainty quantification, machine learning, fluid mechanics. Experience with scientific object-oriented programming
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researchers: two doctoral researchers (m/f/d): reference number: 25_01_A4 These two positions are initially for a fixed term of three years. The institute’s personnel development plan allocates four years
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on the design and evaluation of innovative data- and machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization
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Max Planck Institute for Mathematics in the Sciences | Leipzig, Sachsen | Germany | about 17 hours ago
, but the postdoc has the possibility to teach an advanced course in our international graduate school. We provide a scientific environment where you are encouraged to explore new scientific directions
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consisting of biologists, MDs, and bioinformaticians. Our group fosters independence, innovation and collaboration of the next generation of scientists. The position is available for an initial period of two
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Your Job: The PHOENIX - Launch Space Power-to-X joint project plays a central role in aligning the innovation cycles of P2X technologies with the long-term goals of the energy transition
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of these patients. The goal of this project is to combine cutting-edge multi-omics technology, data analytics, machine learning and clinical samples from the human eye to decipher new insights into disease mechanisms