[ESIP-all] Fwd: [AboutHydrology] USGS Position in Machine Learning for Water Science

Peter Fox pfox at cs.rpi.edu
Wed Oct 2 16:26:08 EDT 2019

FYI, pass it on.


> -------- Forwarded Message --------
> Subject:	Fwd: [AboutHydrology] USGS Position in Machine Learning for Water Science
> Date:	Wed, 2 Oct 2019 16:03:54 -0400
> From:	Kevin Rose <kev.c.rose at gmail.com> <mailto:kev.c.rose at gmail.com>
> To:	Rose, Kevin Christopher <rosek4 at rpi.edu> <mailto:rosek4 at rpi.edu>
> ---------- Forwarded message ---------
> From: Alison Appling <aappling.usgs at gmail.com <mailto:aappling.usgs at gmail.com>>
> Date: Wed, Oct 2, 2019 at 11:25 AM
> Subject: [AboutHydrology] USGS Position in Machine Learning for Water Science
> To: AboutHydrology <abouthydrology at googlegroups.com <mailto:abouthydrology at googlegroups.com>>
> The USGS water data science group in Middleton, Wisconsin is seeking applicants to the following permanent position for a Mathematical Statistician with expertise in machine learning. Background in water sciences is appreciated but not required. Please share this opportunity with any colleagues who might be interested.
> https://www.usajobs.gov/GetJob/ViewDetails/547203300 <https://www.usajobs.gov/GetJob/ViewDetails/547203300>
> Applications will be accepted October 1-14, 2019.
> As a Mathematical Statistician within the Integrated Information Dissemination Division of the Data Science Branch, some of your specific duties will include:
> • Researches existing machine learning techniques, and applies appropriate methods towards prediction.
> • Applies deep learning frameworks (e.g., PyTorch, TensorFlow) to a variety of problems, including time-series modeling and outlier detection.
> • Engages colleagues on hybrid modeling techniques, including efforts to combine theory and empirical (e.g., machine learning) models.
> • Employs analytical, mathematical, and statistical theories and practices to design appropriate machine learning modeling approaches.
> • Builds training, validation, and testing datasets in a robust and reproducible way to train models and evaluate model performance.
> • Delivers model results in appropriate formats (e.g., reports, visuals, presentations, and structured output data).
> • Conducts research as needed to support modeling studies and prepare written products to convey research-based knowledge and information to a variety of audiences.
> • Writes code to automate model prep, execution, evaluation, and presentation of results.
> Contact Jordan Read (jread at usgs.gov <mailto:jread at usgs.gov>) or Alison Appling (aappling at usgs.gov <mailto:aappling at usgs.gov>) with any questions.
> -- 
> You received this message because you are subscribed to the Google Groups "AboutHydrology" group.
> To unsubscribe from this group and stop receiving emails from it, send an email to abouthydrology+unsubscribe at googlegroups.com <mailto:abouthydrology+unsubscribe at googlegroups.com>.
> To view this discussion on the web visit https://groups.google.com/d/msgid/abouthydrology/057c55d0-4bf4-4a26-a0b4-156f5dd3c648%40googlegroups.com <https://groups.google.com/d/msgid/abouthydrology/057c55d0-4bf4-4a26-a0b4-156f5dd3c648%40googlegroups.com?utm_medium=email&utm_source=footer>.
> -- 
> ______________________________________
> Kevin C. Rose, Ph.D.
> Assistant Professor & Frederic R. Kolleck '52 Career Development Chair in Freshwater Ecology
> Department of Biological Sciences
> Rensselaer Polytechnic Institute, Troy, NY 12180
> Office: +1 518-276-8288
> Web: https://science.rpi.edu/biology/faculty/kevin-rose <https://science.rpi.edu/biology/faculty/kevin-rose>

-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.esipfed.org/pipermail/esip-all/attachments/20191002/65fe99ff/attachment.htm>

More information about the ESIP-all mailing list