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Field
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. Required qualifications: PhD in a field such as physics, systems biology, applied mathematics, machine learning, or related fields. Strong programming skills (e.g. Python) and experience with modern ML
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information, agronomic data, and artificial intelligence methods. Assess drought stress and identify physiological traits associated with drought tolerance using advanced imaging technologies. Develop machine
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candidates whose expertise falls within one or more of the following areas: computational and mathematical modeling, statistical modeling, machine learning, network science, bioinformatics, applied mathematics
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subject. Proven experience with X-ray imaging/diffraction techniques, e.g. XRD-CT, 3D-XRD, µ/nanoCT, STXM or similar. Experience in computer programming for data analysis, e.g. Python. Demonstrated ability
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related subject. Proven experience with X-ray imaging techniques, e.g. µCT, nanoCT, TXM or similar. Experience in computer programming for data analysis, e.g. Python. Demonstrated ability to work both
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domains such as telecom, defence and cloud. You will join the Machine-Intelligence for Networks and Distributed Systems (MINDS) research group at the Department of Computing and Learning Systems, School
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data and multimodal datasets combining imaging and molecular measurements. Have a background in computational biology, bioinformatics, or machine learning for biological problems. Have experience with
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science, computer programming or related field with a genuine passion for transport challenges and innovation. This eligibility requirement must be met no later than the time the employment decision is made
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required to meet the eligibility criteria. Of secondary importance are: Experience with Python and relevant libraries for machine learning, optimization and simulation. Documented expertise in simulation
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organisations, military service or similar circumstances, as well as clinical practice or other forms of appointment/assignment relevant to the subject area. Doctoral degree should be within molecular biology