The Max Planck Institute for Gravitational Physics (Albert Einstein Institute) in
Potsdam-Golm invites applications for two postdoctoral positions in the
gauge-gravity duality (holography). The researchers will join the newly-formed
independent research group “Gauge-Gravity Duality” led by Michal P. Heller
and generously supported by the Humboldt Foundation through the Sofja
Kovalevskaja Award. The group is primarily focused on exploring quantum
information approaches to quantum field theories and gravity in the context of
holography and beyond. Other topics of interest include non-equilibrium
physics of gauge theories, foundations of relativistic hydrodynamics,
numerical holography and various aspects of black hole physics.
The initial appointment will be for two years with a possibility of extension for
one more year upon exceptional performance. In outstanding cases threeyear
contract offer can be made. The positions come with very competitive
research budgets. The group members will also benefit from active visitor and
seminar agenda, several topical workshops organized in the Berlin area, as
well as strong ties with the Division of Quantum Gravity and Unified Theories.
The starting date is negotiable with the default date of appointment being
September 2017. The group is expected to grow to at least five researchers
by the end of 2018.
Interested applicants are invited to send their materials through the regular
application system of the Division of Quantum Gravity and Unified Theories
available at:
https://jobs.itp.phys.ethz.ch/postdoc/
The deadline for applying is 1 December 2016. See this link for the details of
the application procedure.
For more information, please send an e-mail to
michal.p.heller@aei.mpg.de
Don’t regard the action’s speed but follow the good performance of a task, for people don’t ask how long it does take but rather they ask about its quality
Imam Ali a.s
Beautiful saying of Imam Ali {AS} from Nahjul Balagha
لا تَطْلُبْ سُرعَةَ العَمَلِ و اطْلُبْ تَجوِیدَهُ؛ فإنَّ الناسَ لا یَسألُونَ: فی کَم فَرَغَ مِنَ العَمَلِ، إنّما یَسألُونَ عَنْ جَوْدَةِ صَنعَتِهِ.
در پی سرعت عمل مباش، بلکه به دنبال خوب انجام دادن آن باش، زیرا مردم نمی پرسند در چه مدت کار را به سرانجام رساند، بلکه از کیفیت انجام آن می پرسند.
Advanced cancer research is calling on techniques used by NASA scientists who analyze satellite imagery to find commonalities among stars, planets and galaxies in space.
Scientists from NASA's Jet Propulsion Laboratory (JPL) use complex machine learning algorithms to identify similarities among galaxies that may otherwise be overlooked, NASA officials said in a statement. Using similar techniques, medical professionals are able to analyze a lung sample for common cancer biomarkers.
However, analyzing a biopsy specimen for biomarkers is not the only way in which JPL's complex machine learning algorithms can be used in the medical field. Cancer researchers can also use the space exploration tools to identify common chemical or genetic signatures related to specific cancers, which could revolutionize strategies for early cancer detection. [Captain Kirk Says Space Exploration Leads To Medical Advancements (Video)]
In a continuing effort to advance both space and medical knowledge, JPL and the National Cancer Institute (NCI) renewed a research partnership on Sept. 6, which they expect will carry through 2021. By compiling their collective findings into a searchable network, researchers from the NCI-supported Early Detection Research Network (EDRN) hope to improve early diagnosis of cancer or cancer risk by distributing their findings across the world, much like astronomers have done, NASA officials said in the statement.
This photo captures galaxies known as NGC 3718 and NGC 3729. NASA scientists are able to identify similarities among these galaxies and others like them using machine learning algorithms.
Credit: Catalina Sky Survey, U of Arizona, and Catalina Realtime Transient Survey, Caltech
"From a NASA standpoint, there are significant opportunities to develop new data science capabilities that can support both the mission of exploring space and cancer research using common methodological approaches," Dan Crichton, the head of JPL's Center for Data Science and Technology, said in the statement. "We have a great opportunity to perfect those techniques and grow JPL's data science technologies, while serving our nation."
Using the algorithms designed for space exploration, EDRN researchers have already discovered six new Food and Drug Administration-approved chemical and genetic signatures that denote the presence of cancer, called biomarkers, and nine biomarkers approved for use in Clinical Laboratory Improvement Amendments labs, according to the statement from NASA.
In addition to the EDRN, the renewed partnership will assist other NCI-funded programs, such as the Consortium for Molecular and Cellular Characterization of Screen-Detected Lesions and the Informatics Technology for Cancer Research Initiative.
Making medical data accessible across the globe will solve a common problem with uniformity. Previously, medical data such as patient age, cancer type or other characteristics was not labeled and stored uniformly (the same everywhere), so it could not be shared and studied by everyone, NASA officials said.
"We didn't know if they were early-stage or late-stage specimens, or if any level of treatment had been tried," Sudhir Srivastava, chief of NCI's Cancer Biomarkers Research Group and head of EDRN, said in the statement. "And JPL told us, 'We do this type of thing all the time! That's how we manage our Planetary Data System.'"
In the years to come, the NCI plans to incorporate image-recognition technology to help archive images of cancer specimens from the EDRN. Then, much like how computer algorithms comb through images of star clusters,these images could be analyzed for early signs of cancer based on a patient's age, ethnic background and other demographics, Crichton said in the statement.
"As we develop more automated methods for detecting and classifying features in images, we see great opportunities for enhancing data discovery," Crichton added. "We have examples where algorithms for detection of features in astronomy images have been transferred to biology and vice versa."
A lung specimen analyzed for common cancer biomarkers
Credit: Early Research Detection Network/University of Colorado
