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Field
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determined by the funding available. About you You should hold, or be near completion of, a PhD/DPhil in Molecular Microbiology, Biochemistry, Biophysics or related discipline. You must have substantive
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, systems biology, applied mathematics, nuclear medicine physics, or a related field. Prior experience in at least one of the following is a requirement: physiologically based pharmacokinetic (PBPK) modeling
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and the Linux programming environment. Candidates with background in Physics, Physical Chemistry and Engineering are desirable. Candidates with only prior experience in empirical methods like docking
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, Machine Learning, Physics, Mathematics, Engineering, or equivalent A solid publication record with research publication(s) in peer-reviewed international journals Good programming skills (ideally in Python
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, Machine Learning, Physics, Mathematics, Engineering, or equivalent A solid publication record with research publication(s) in peer-reviewed international journals Good programming skills (ideally in Python
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selection process with applications accepted year-round. Eligibility requirements: Candidates should hold a PhD degree in computer science, data science, mathematics, physics, statistics, or electrical
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for a hard-working candidate. Qualifications Required skills A PhD degree within statistical genetics, applied mathematics, computer science, medicine, psychology, molecular genetics, or similar. Advanced
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of applications will begin immediately and continue until the position is filled. Required Qualifications - Ph.D. in either Quantum Information Science, Computer Science, Mathematics, Theoretical Physics or a
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, computer science, electrical engineering, applied mathematics, or operations research) before May 2025 are encouraged to apply. Ideal candidates will display outstanding ability for research and a record of
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materials such as the magnetoelectric, high entropy oxides, through neutron scattering experiments. Additionally, collaborative work will be performed with the aim of developing and applying machine learning