The DIRAC Institute in the Department of Astronomy at the University of Washington is seeking applicants with a strong research record in the development of statistical techniques or algorithms for analyzing large astrophysical data sets for two postdoctoral positions.
AstroML: The first position is to help in the development of the second edition of astroML (http://astroml.org) a popular Python-based machine learning package for astrophysics. New components we are incorporating within astroML include methodologies from deep learning and hierarchical bayesian statistics. Special emphasis will be placed on building a broader community and making astroML a sustainable open-source project. The successful candidate will lead these activities, including the application of the new codes to dataset available to UW researchers.
Time Series Data: The second position is to develop new approaches for analyzing astronomical time series data using modern computational frameworks. The goal of this framework will be to enable science with the ZTF and LSST data sets. Promising applicants should possess an interest in time domain science and experience or interest in the use of databases and large scale compute platforms such as Spark, Dask, or similar. Good Python skills, and experience with machine learning libraries, image processing of astronomical images, or astronomical databases are desirable.
The DIRAC Institute is a newly formed center for data intensive astrophysics at the University of Washington. The Institute consists of six faculty and senior fellows, and over 20 postdoctoral researchers and research scientists. It has active research programs in Cosmology, Solar System science, Milky-Way structure, the Variable and Transient universe, andAstronomical Software.
The University of Washington is a partner in the Zwicky Transient Facility (ZTF) project, a new time-domain survey which will begin operations in early 2018. The UW is a founding partner of the LSST project, and leads the construction of its time domain and solar system processing pipelines. Other research activities at UW/DIRAC include topics in extragalactic science, as well as the understanding the structure, formation, and evolution of the Milky Way using large surveys (SDSS, WISE, PanSTARRS PS1, and others).
A Ph.D. degree in astronomy, physics, computer science, or a related subject is required. The initial appointment is for two years, renewable up to three years, and offers competitive salary and benefits. The appointments are available immediately and are expected to start no later than September 2018.
Applicants should submit a curriculum vitae, description of research interests (with links to Github if relevant) and arrange for three letters of reference to be submitted to Nikolina Horvat at firstname.lastname@example.org with subject line “DIRAC postdoc application (your name)”. Applications will be accepted until the positions are filled, to assure full consideration, please send your application by Dec 31st 2017
For detailed information about the benefits available through the University of Washington, including dental, medical and disability insurance, retirement, and childcare centers, see the University of Washington benefits page: https://www.washington.edu/admin/hr/benefits/.
The DIRAC Institute is a community of people with diverse interests and areas of expertise, engaged in the understanding of our universe through the analysis of large and complex data sets. We are an open, ethical, highly engaged and collaborative community based on trust, transparency and mutual respect. We believe in providing a welcoming and inclusive environment, in the importance of quality of life, in embracing diversity, in making a difference and having fun.
Petar Zečević is a PhD student from University of Zagreb, Croatia. He has been working in the software industry for more than 15 years, as a full-stack developer, consultant, analyst, and team leader. Petar is the author of Spark in Action book (Manning, September 2016). He also gives talks on Apache Spark, organizes monthly Apache Spark Zagreb meetups, and has several Apache Spark projects behind him.
Thank you again for participating in the inaugural DIRAC Institute Open House!
2011 Nobel Laureate, Dr. Saul Perlmutter inspired us by his lecture
“What We Learn When We Learn the Universe is Accelerating”.
Saul Perlmutter is a 2011 Nobel Laureate, sharing the prize in Physics for the discovery of the accelerating expansion of the Universe. He is a professor of physics at the University of California, Berkeley, where he holds the Franklin W. and Karen Weber Dabby Chair, and a senior scientist at Lawrence Berkeley National Laboratory. He is the leader of the international Supernova Cosmology Project, and director of both the Berkeley Institute for Data Science and the Berkeley Center for Cosmological Physics. His undergraduate degree was from Harvard and his PhD from UC Berkeley. In addition to other awards and honors, he is a member of the National Academy of Sciences and the American Academy of Arts and Sciences and a fellow of the American Physical Society and the American Association for the Advancement of Science. Perlmutter has also written popular articles, and has appeared in numerous PBS, Discovery Channel, and BBC documentaries. His interest in teaching scientific approaches to problem-solving for non-scientists led to Berkeley courses on Sense and Sensibility and Science and Physics & Music.
The 1998 discovery that the universe’s expansion is accelerating was not only unexpected, but also led to the idea of a previously-unknown “dark energy” forming almost three-quarters of the “stuff” that makes up the universe. How was this discovery made? How did new ways of thinking about the collection of data make this discovery possible? What has been the progress since in understanding this dark energy, the accelerating universe, and potentially the fate of the universe? In this illuminating and provoking talk Dr. Saul Perlmutter, Nobel Laureate, will describe the observations that led to the discovery of “dark energy” and how our ability to collect data at ever increasing rates is changing the way we undertake science and and the discoveries we can make.
When: February 20, 2018 @ 12:30 – 1:30pm
Where: PAB, B305, 3rd floor
The challenge of the distributed Euclid survey:
The ESA Euclid space mission core science goals rely on complementary deep ground-based surveys covering both the northern and southern galactic caps. This talk will present an overview of the space survey design and the current status of the ground-based survey campaign, with a focus on its first element, CFHT’s Canada-France Imaging Survey.
Eddie is a Hubble Fellow working at Lawrence Berkeley National Laboratory, trying to understand the structure of the Galaxy, and especially its dust, using observations of stars. His most recent work uses the DECam instrument on the Blanco 4-m telescope to image the southern Galactic plane, to understand its stars, gas, and dust. He also uses APOGEE spectroscopy to understand how dust properties vary, and the PS1 survey to infer the three-dimensional structure of the dust in the Milky Way.
When: February 14, 2018 @ 2:00-3:00pm
Where: PAB, WRF Data Science Studio, Seminar Room, 6th floor
The DECam Plane Survey: Optical Photometry of Two Billion Objects in the Southern Galactic PlaneThe DECam
Plane Survey is a five-band optical and near-infrared survey of the southern Galactic plane with the Dark Energy Camera at Cerro Tololo. The survey is designed to reach past the main-sequence turn-off of old populations at the distance of the Galactic center through a reddening E(B − V ) of 1.5 mag. Typical single-exposure depths are 23.7, 22.8, 22.3, 21.9, and 21.0 mag (AB) in the grizY bands, with seeing around 1″. The footprint covers the Galactic plane with |b| < 4 degrees, 5 degrees > l > −120 degrees. The survey pipeline simultaneously solves for the positions and fluxes of tens of thousands of sources in each image, delivering positions and fluxes of roughly two billion stars with better than 10 mmag precision. Most of these objects are highly reddened and deep in the Galactic disk, probing the structure and properties of the Milky Way and its interstellar medium. The fully processed images and derived catalogs are publicly available.
Read more about Eddie here.
When: February 12, 2018 @ 12:30 – 1:30pm
Where: PAB, WRF Data Science Studio, 6th floor
Big Data Cosmology with Subaru HSC Survey
Subaru HSC is ongoing and is planned to survey a total of ~1200 square degrees by 2019. We have already conducted 180-nights observation, and collected about 65% of the total. We use the data to detect and classify distant supernovae and to reconstruct the large-scale cosmic density field in 3D. To this end, we have developed a new “machine” adopting AUC boosting and pAUC methods, as well as commonly used Random Forest and DNN. The machine detected ~1500 supernovae including faint ones down to 26-th magnitude. We have also developed a multi-label classifier (Type Ia, Ibc, IIP, IIN, IIL) and used it successfully to extract a few tens high-redshift Type Ia supernovae, which have been sent for spectroscopic observations by HST. For cosmological parameter estimation, we have developed a fast, machine-learned “emulator” that calculates statistical quantities of weak lensing. We run 200 supercomputer simulations of cosmic structure formation and use the outputs to train and develop the emulator (effectively a python package). Cross-validation study shows that the emulator predicts the gravitational lensing effects on the matter distribution and on the clustering of galaxies with an accuracy of 3 percent. We will integrate the emulator in our Markov-Chain Monte-Carlo program to infer the main cosmological parameters such as the matter density and the density fluctuation amplitude. Finally, we are developing a CNN that can calculate basic strong lensing parameters such as Einstein radius and lens ellipticity from observed multi-band images. I discuss the future prospects for LSST.
When: February 5, 2018 @ 12:30-1:30pm
Where: PAB, WRF Data Science Studio, 6th floor
Mini-Workshop: Alert Stream Filters for Your ZTF Science Goals
February 1-2, 2018.
I am currently an associate professor in the Astronomy Group in the Physics and Astronomy Department at Vanderbilt University. My research interests lie in the areas of large-scale structure and galaxy formation, as well as ultra-high energy cosmic rays. I completed my Ph.D. degree in Astronomy at the Ohio State University, and my A.B. degree inAstrophysical Sciences at Princeton University. Before that, I lived in Athens, Greece where I attended Athens College.
When: February 2, 2018 @ 1:00-2:00pm
Where: PAB, 3rd floor, B305
From Dark Matter to Galaxies: Probing the Spatial Structure of the Universe on Small Scales
The last decade has seen an explosion of high precision measurements of the structure of the universe, courtesy of large galaxy surveys such as the Sloan Digital Sky Survey (SDSS). Galaxy clustering measurements encode information about the nature and abundance of dark matter and dark energy, as well as the complex physical process of galaxy formation. However, harnessing the full constraining power of the data is very challenging since it requires a detailed understanding of the statistical and systematic uncertainties in both data and models, which in turn demands significant computational effort. I will discuss my ongoing research program to analyze SDSS data and model it with the help of cosmological N-body simulations, highlighting results from both recent and ongoing projects.