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on thalamo-hippocampal circuits underlying social aggression and pain regulation, with the goal of uncovering novel mechanisms that shape complex behaviors. This is an excellent opportunity for individuals
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, developing optogenetic and CRISPR-based logic circuits, and using Raman spectroscopy to monitor cellular responses and validate model predictions. The work will directly inform the design of multiplexed
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the following two research directions: - Electrochemical CO2 reduction: electrocatalyst development; flow reactor design; integrated CO2 capture and reduction. - Electrochemical NOx reduction and building C-N
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. Required Qualifications • Ph.D. in Aerospace or Mechanical Engineering. PhD must be awarded no more than four years prior to the effective date of appointment with a minimum of one year eligibility remaining
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of the role will include but may not be limited to the following: • Neurophysiology & Imaging: Perform in vivo electrophysiology and optical recordings. • Animal Behavior: Design and conduct behavioral
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Construction Engineering, Civil Engineering, Mechanical Engineering, Computer Science, Robotics, or a related field. Ph.D. in relevant engineering degree. PhD must be awarded no more than four years prior
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alternative data sources, maintaining detailed design specification and written documentation for existing models, being responsible for the development of new computational methods and work with programmers
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on their research and innovation campus. Required Qualifications - PhD and/or MD in Computational Biology, Bioinformatics, Genomics, Biology, Data Science, Computer science or other related fields. PhD must be
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recruiting a Postdoctoral Associate in Biochemistry and Metabolic Engineering of Volatile Natural Products. The Tholl lab integrates biochemical, molecular, and genomic tools to investigate the metabolism
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molecular dynamics simulations. This position emphasizes research in the modeling of complex chemical systems, where the candidate will integrate advanced simulation techniques with modern machine learning