303 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Nature Careers
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meets the following criteria: Recently earned a Ph.D. in bioinformatics, computational biology, computer science, electrical and computer engineering, or a related discipline Strong experience in
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metabolomes (targeted and untargeted) and plant transcriptomes In addition to collaborating with project partners, you will join an active and expanding community of PhD and Postdoc students in the plant
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/10.1126/science.adm8203. 2. Keleş, M.F., Sapci, A.O.B., Brody, C., Palmer, I., Mehta, A., Ahmadi, S., Le, C., Tastan, Ö., Keleş, S., and Wu, M.N. (2025). FlyVISTA, an Integrated Machine Learning Platform
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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
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collaborate with PhD and other post doctoral fellows, in cross-disciplinary collaborations with other research groups both national and international as well as with industrial partners. Qualifications A PhD
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5,000 square metres, including innovations in all that we do An environment encouraging curiosity, innovation and entrepreneurship in all areas Personalized learning programme to foster our staff’s soft
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electrical engineering, automation, computer engineering, or a closely related discipline, with proven expertise in the operation and control of intelligent energy systems. Experience and skills Solid
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, supervisor, and scientific community, promoting the lab's reputation, write reports for sponsors, research grants, and submit publications to journals Teach, supervise, and mentor undergraduate, PhD, and
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transfer our knowledge to society. We are doing this according to our vision: “Taking the pulse of our Earth to safeguard a habitable planet”. For the ERC-funded project RockDeath (Role of Fluids in rock
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Postdoctoral researcher (f_m_x) - Waves in the Inner-magnetosphere and their Effects on Radiation...
of the adverse effects of the space environment utilizing satellite observations, physics-based numerical models, machine learning, and data assimilation. Our research will help safely design and operate