[ES_JOBS_NET] AI+climate postdoc + 2 PhD students, JHU

[Karen McKinnon]

Please see below for information about *both *an open postdoctoral
position, and two fully-funded PhD positions in my group at Hopkins.


Postdoctoral position in AI and climate


The Climate Intelligence Lab at Johns Hopkins University, led by Professor
Karen McKinnon, is hiring for a postdoctoral scholar to work at the
intersection of AI and climate.


The initial focus of the postdoc will be developing, validating, and
analyzing AI-based observational large ensembles using emulators to
quantify risk of low-probability, high-impact extremes. The validation will
focus both on the statistics of the ensembles and analysis of the physical
mechanisms that lead to the extremes in the AI ensembles.


There is also scope, based on the interests of the postdoc, to develop
frameworks for uncertainty quantification for AI weather, S2S, and climate
models, and flexibility to work on emerging problems in the field.


The position is in-person, and there is support for conference travel and
other professional development.


The required qualifications are:

   -

   A PhD in atmospheric science, climate science, Earth science, or related
   fields; or a PhD in computer science, applied math, statistics, or related
   fields with demonstrated interest and publications in climate science
   -

   Strong quantitative skills
   -

   Fluency in Python
   -

   Excellent oral and written communication skills, including scientific
   writing
   -

   Demonstrated independence as a researcher


The ideal candidate will also have demonstrated deep knowledge of and/or
experience developing AI emulators for geophysical or related data.


The position is for two years, subject to renewal after one year, and may
be extended further subject to funding availability and fit.


To apply, please email Prof. McKinnon at karen.mckinnon at jhu.edu.

   -

   Subject line: AI/climate postdoc application
   -

   Cover letter (pdf, one page) explaining your interest and fit to the
   position
   -

   CV, including at least degrees, publications, and awards (pdf)
   -

   A list of 3 references, including your relationship to them and their
   email address


Application review will begin on September 10. The position could begin as
soon as November 1, with an ideal start date between November 1 and March
1.



Fully-funded PhD positions in climate science (The Climate Intelligence
Lab, JHU)


The Climate Intelligence Lab at Johns Hopkins University, led by Professor
Karen McKinnon, is recruiting two fully funded PhD students, to begin Fall
2027, in the Department of Environmental Health and Engineering within the
Whiting School of Engineering.


One of the positions will focus on variability and extremes in AI weather
and climate emulators, and could include additional foci on uncertainty
quantification, mechanistic evolution of weather extremes, and/or S2S
predictions.


The second position will focus on climatic controls and projections of
western US hydroclimate, including wildfire, drought, and streamflow. The
research will rely on developing predictive, causal models using historical
observations, and identifying robust future changes using climate models.


In your application, please indicate which project is of greater interest,
and your relevant background. Applications should be submitted to the
Environmental Engineering program through the Whiting School of
Engineering. Strong candidates will be contacted in the winter, after
applications are received, for interviews.


For both positions, the minimum requirements are:

   -

   BS or BA in atmospheric science, climate science, Earth science, or
   related fields; or a BS or BA in computer science, applied math,
   statistics, or related fields with demonstrated interest in climate science
   -

   Strong quantitative skills
   -

   Experience and competency with Python or another scientific computing
   language
   -

   Curiosity and a desire to collaborate within the research group


The preferred qualifications include:

   -

   A master’s degree or work experience
   -

   Fluency in Python
   -

   Experience working with large climate datasets using Linux systems


   -

   One or more publications in a peer-reviewed journal
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