20 computer-science-programming-languages-"the"-"FEMTO-ST-institute"-"U" Postdoctoral positions at Princeton University
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, working under the guidance of Dr. Arash Adel, Assistant Professor in the School of Architecture and Associated Faculty of the Department of Computer Science. The desired start date is Spring 2025
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evaluation.This opportunity will prepare candidates for a range of competitive positions in academia or industry that involve computational biology/chemistry, machine-learning for biological or chemical data, and
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applicants will have:*Expertise conducting spatial and statistical analyses*Experience with scientific computer programming in R and Python*Formal training or experience applying quantitative and spatial
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Engineering, Computer Science/Engineering, Data Science, or a closely related field *Proficiency in Python or other tools and ML frameworks *Track record of open source contributions or tool development in AI
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of squamate reptiles; the largest group of terrestrial vertebrates on Earth today with 11,000 species. A Ph.D. in Evolutionary Biology, Computational Biology, or related fields, is required. The work will focus
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computer programming in R and Python *Formal training or experience applying quantitative and spatial methods to human-environment questions *Excellent academic writing and communication skills in English
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, experience with a variety of programming languages, and familiarity with critical path planning tools, are essential. A Ph.D. in engineering, operations research, computer science, or another related field is
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microscopy and/or structural biology; Fluent in English language and writing skills. Some experience in cryo-EM or eukaryotic protein expression is a plus.The successful candidate will join a highly
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comprehensive benefit program to eligible employees. Please see this link for more information.
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of terrestrial vertebrates on Earth today with 11,000 species. A Ph.D. in Evolutionary Biology, Computational Biology, or related fields, is required. The work will focus on i) phylogenomic inference of hundreds