SMB Digest’s What’s New in The Queue

From: Henry Warchall <hwarchal@nsf.gov>

This is a reminder of upcoming important dates for the NSF Mathematical Sciences Research Institutes program solicitation (NSF 17-553). Please see www.nsf.gov/funding/pgm_summ.jsp?pims_id=5302 for details.

Letter of Intent (required) deadline: December 14, 2018
Full Proposal deadline: March 14, 2019
(Anticipated subsequent Full Proposal deadline: March 14, 2024)

From the program solicitation:

Mathematical Sciences Research Institutes are large-scale projects that collectively have several important impacts:
* Institutes advance research in the mathematical sciences, encourage research that is timely and potentially transformative, and assist rapid and broad dissemination of new ideas;
* Institutes focus effort and excellence in the mathematical sciences, operating on a national scale to reach across the mathematical disciplines, to explore emerging frontiers of those disciplines, and to engage with scientific opportunities in other fields;
* Institutes provide intellectual infrastructure for research collaborations within the mathematical sciences and at the interface of the mathematical sciences and other disciplines;
* Institutes increase the impact of the mathematical sciences in other disciplines by sponsoring interdisciplinary activities and enhancing synergistic approaches to significant scientific problems;
* Institutes provide opportunities for students and postdoctoral fellows to interact with leading researchers;
* Institutes support the exchange of information with business, industry, government, and national laboratories, providing access to expertise in the mathematical sciences;
* Institutes demonstrate leadership in promoting diversity in the mathematical sciences enterprise;
* Institutes provide opportunities for outreach to the scientific community and the public at large;
* Institutes play an important role in fostering international collaborations.

The Division of Mathematical Sciences invites proposals for projects that contribute to this important, influential activity. This competition welcomes new projects from U.S. sites as well as renewal proposals from any of the U.S.-based institutes that have had previous funding from NSF.

—————————————————-

From: Catherine Crawley <ccrawley@nimbios.org>

The National Institute for Mathematical and Biological Synthesis (NIMBioS) is now accepting applications for its Tutorial, “Network Modeling,” to be held February 4-6, 2019, at NIMBioS.

Objectives: This tutorial aims to introduce faculty, post-docs & graduate students to the topic of complex networks. The field has grown tremendously over the last 20 years and network science has found numerous applications to fields such as biology, ecology, social sciences, physical sciences, computer science, technology, and urban planning. The tutorial will review in detail the main ideas and methods in network science with emphasis on both conceptual aspects and real-world applications. Hands-on activities will help the audience familiarize themselves with practical ways to potentially incorporate network analysis in their own research. No prior knowledge of networks or programming is required.

Location: NIMBioS at the University of Tennessee, Knoxville

Co-Organizers: Nina Fefferman , Ecology & Evolutionary Biology, Univ. of Tennessee; Lazaros Gallos, DIMACS, Rutgers Univ.; Gonzalo Suarez, Ecology & Evolutionary Biology, Univ. of Tennessee

For more information about the tutorial and a link to the online application form, go to www.nimbios.org/tutorials/TT_networks

Participation in NIMBioS tutorials is by application only. Individuals with a strong interest in the topic are encouraged to apply, and successful applicants will be notified within two weeks after the application deadline. Limited travel support is available for those with a demonstrated need.

Application deadline: November 18, 2018

The National Institute for Mathematical and Biological Synthesis (NIMBioS) (www.nimbios.org) brings together researchers from around the world to collaborate across disciplinary boundaries to investigate solutions to basic and applied problems in the life sciences. NIMBioS is supported by the National Science Foundation, with additional support from The University of Tennessee, Knoxville.

—————————————————-

From: Catherine Crawley <ccrawley@nimbios.org>

The National Institute for Mathematical and Biological Synthesis (NIMBioS) is now accepting applications for its Tutorial, “Network Modeling,” to be held February 4-6, 2019, at NIMBioS.

Objectives: This tutorial aims to introduce faculty, post-docs & graduate students to the topic of complex networks. The field has grown tremendously over the last 20 years and network science has found numerous applications to fields such as biology, ecology, social sciences, physical sciences, computer science, technology, and urban planning. The tutorial will review in detail the main ideas and methods in network science with emphasis on both conceptual aspects and real-world applications. Hands-on activities will help the audience familiarize themselves with practical ways to potentially incorporate network analysis in their own research. No prior knowledge of networks or programming is required.

Location: NIMBioS at the University of Tennessee, Knoxville

Co-Organizers: Nina Fefferman , Ecology & Evolutionary Biology, Univ. of Tennessee; Lazaros Gallos, DIMACS, Rutgers Univ.; Gonzalo Suarez, Ecology & Evolutionary Biology, Univ. of Tennessee

For more information about the tutorial and a link to the online application form, go to www.nimbios.org/tutorials/TT_networks

Participation in NIMBioS tutorials is by application only. Individuals with a strong interest in the topic are encouraged to apply, and successful applicants will be notified within two weeks after the application deadline. Limited travel support is available for those with a demonstrated need.

Application deadline: November 18, 2018

The National Institute for Mathematical and Biological Synthesis (NIMBioS) (www.nimbios.org) brings together researchers from around the world to collaborate across disciplinary boundaries to investigate solutions to basic and applied problems in the life sciences. NIMBioS is supported by the National Science Foundation, with additional support from The University of Tennessee, Knoxville.

—————————————————-

From: Eric Maki <eam@ams.org>

Mathematical Biology: Modeling and Analysis, by Avner Friedman, is now available from the American Mathematical Society.

The fast growing field of mathematical biology addresses biological questions using mathematical models from areas such as dynamical systems, probability, statistics, and discrete mathematics.

This book considers models that are described by systems of partial differential equations, and it focuses on modeling, rather than on numerical methods and simulations. The models studied are concerned with population dynamics, cancer, risk of plaque growth associated with high cholesterol, and wound healing. A rich variety of open problems demonstrates the exciting challenges and opportunities for research at the interface of mathematics and biology. The book primarily addresses students and researchers in mathematics who do not necessarily have any background in biology and who may have had little exposure to PDEs.

For more information, including a complete table of contents, please visit bookstore.ams.org/cbms-127/

—————————————————-

From: Morten Gram Pedersen <pedersen@dei.unipd.it>

It is with pleasure that we invite you to attend the thematic meeting of the Biophysical Society “Quantitative Aspects of Membrane Fusion and Fission”, which will be held in Padova, Italy on May 6-10, 2019.

This meeting will bring together experimentalists and modelers/theorists in the field of membrane fusion and fission. Quantitative understanding of biophysical mechanisms increasingly requires analysis of dynamical and physiologically relevant cellular changes. This is especially relevant for biological membrane processes that occur at distinct points in time and space, such as membrane fusion or fission, and that are driven by localized and quantifiable interaction of proteins, lipids, and messenger molecules.

This interdisciplinary meeting will address the growing need for collaboration between experimentalists and theorists to fully take advantage of the quantitative nature of the experimental observations in this field and to improve the quantitative descriptions of membrane events. We hope to attract applied statisticians, mathematical modelers, and experimentalists investigating membrane fusion and fission, such as regulated exocytosis, endocytosis, and mitochondrial fusion and fission, among others.

Visit the website (www.biophysics.org/2019padova) for the program overview and the list of speakers. Contributed talks will be selected from submitted abstracts. We especially encourage submissions from graduate students and postdoctoral researchers.

Submit an abstract by the January 14, 2019 deadline, and register at the lower early registration rate by February 1, 2019. Biophysical Society members are entitled to significant savings on all registration rates.

—————————————————-

From: NIH Extramural Nexus (NIH/OD) <ExtramuralNexus@mail.nih.gov>

See list.nih.gov/cgi-bin/wa.exe?A2=ind1810&L=extramuralnexus&F=&S=&P=67.

—————————————————-

From: Owens, Roland (NIH/OD) [E] <owensrol@mail.nih.gov>

Staff Scientist Social Epidemiology Research Unit, NHGRI
(deadline: October 31)

The NIH National Human Genome Research Institute seeks a Staff Scientist for
its Social Epidemiology Research Unit. The successful candidate will join a
lab comprised of population-based epidemiologists focused on cardiovascular
phenotypes. The individual will be engaged in social epidemiology research
assessing the relationship of social factors on cardiovascular phenotypes and
related mechanisms. The individual will also be engaged in human social
genomics and social epigenetics based on RNA, transcriptomic, methylation and
mechanisms related to social exposure. Human social genomics/epigenetics is a
new field of genomics that examines the effect of exposure to social factors on
gene expression. This research includes genomic, clinical, socio-demographic,
and social factors (i.e. socioeconomic status, perceived stress, neighborhood
characteristics). The conceptual framework seeks to understand the influence of
exposome on gene expression. Machine learning, as well as classical modelling
techniques, will be employed.

The Staff Scientist, under the direction of the PI, will manage the lab,
supervise technical staff and research trainees and oversee the general
operation of the laboratory. The Staff Scientist will develop an independent
research program based on the portfolio of the lab. The individual will
collaborate with other scientists in the lab, the Branch, NHGRI and throughout
NIH. The individual will also be expected to oversee all databases in the lab.

Qualifications: Qualified candidates should be highly motivated and have a
doctoral degree with training in population-based epidemiology with exposure to
genomics research. Four years of previous post-doctoral research experience is
highly desirable. The teamwork, research and oversight role of the Staff
Scientist requires initiative, organizational skills, attention to detail, good
interpersonal skills, and effective communication. To Apply: Interested
applicants should submit their cover letter, curriculum vitae and contact
information for three references to: sharon.davis@nih.gov. Please indicate
“Applicant for Staff Scientist” in the email subject heading. The closing date
for all applications is October 31, 2018.

Chief Biostatistics and Bioinformatics Branch, NICHD
(Review of applications begins: November 1)

The Division of Intramural Population Health Research of the Eunice Kennedy
Shriver National Institute of Child Health and Human Development is recruiting
a Senior Investigator to serve as Chief of the Biostatistics and Bioinformatics
Branch (BBB).

The BBB mission is to develop novel biostatistical and bioinformatics methods
motivated by the Division’s population health research that spans human
fecundity and fertility, pregnancy, child and adolescent development, and
health-related behaviors. This includes developing and implementing methods
that support original research seeking to answer critical data gaps, analysis
of databases from completed and ongoing research studies, and dissemination of
statistical tools to the field. Branch scientists actively collaborate on all
etiologic and interventional research in keeping with the team science paradigm
practiced by the Division. The BBB mission highlights mentoring the next
generation of leaders in the field of biostatistics. BBB currently has
methodological research programs in the areas of analysis of biomarker data,
analysis of time-to-event data, and analysis of longitudinal and correlated
data. A description of the Branch may be found here, and an overview of all
Division research is summarized in the Division’s annual report.

The Branch Chief will shape the research direction of BBB and provide
scientific, administrative and fiscal leadership of the Branch while
maintaining his/her own original methodologic and collaborative research. The
successful candidate will be a dynamic leader, an internationally recognized
methodologist whose accomplishments are commensurate with the academic rank of
a tenured full professor as demonstrated by a strong upward trajectory of high
quality statistical publications, a trajectory of high impact collaborative
publications, extramural funding (for academic candidates), and a clear vision
of BBB’s essential role in population health research. Candidates must have an
earned doctoral degree in biostatistics, statistics, bioinformatics, or a
closely related quantitative field. Experience leading trans-disciplinary
scientific teams and recruiting early stage biostatisticians is highly
desirable.

The Chief is expected to lead in strengthening current research areas and in
developing new areas of expertise to address increasingly challenging designs
and analyses, including priorities such as bioinformatics and causal inference
in areas central to the Institute’s mission. The Chief is expected to lead in
developing and implementing the Division’s infrastructure and culture of
reproducible research. The BBB Chief will serve as a member of the Division’s
senior leadership working to further the Division and Institute’s vision and
mission in the area of population health.

The Branch Chief will be eligible for a tenured appointment at a salary
commensurate with his/her credentials and experience. Full federal government
benefits will be provided, including leave, health and life insurance,
long-term care insurance, retirement plan, and savings plan (401k equivalent).
Interested candidates should submit a cover letter describing professional
training and experience in leadership, management, administration, professional
service, and mentoring and outreach activities (especially those involving
women and persons from racial/ethnic or other groups that are underrepresented
in biomedical research); statement of research interests; brief (2-3 pages)
vision statement for the Branch; and the names, affiliations, and contact
details for three references. Please email these materials as one package to:
Ms. Adrienne Lonaberger, Program Analyst, DIPHR, NICHD; 6710B Rockledge Drive;
Room 3241D; Bethesda, MD 20892, or greenad@mail.nih.gov.

All inquiries about the position should be directed to the Committee Chair: Dr.
Stephen Gilman, Chief and Senior Investigator, Social and Behavioral Sciences
Branch, DIPHR, NICHD; 6710B Rockledge Drive; Room 3154; Bethesda, MD 20892, or
301-435-3895 / stephen.gilman@nih.gov. Complete applications received by
November 1, 2018 will be considered for a first round of interviews, but
applications will be accepted until the position is filled. The selected
candidate is expected to assume the position and be onsite by July 1, 2019.
HHS, NIH, and NICHD are equal opportunity employers.

—————————————————-

From: Paul Atzberger <atzberg@math.ucsb.edu>

A postdoctoral position is available to work on problems at the interface of scientific computation and machine learning in the research group of Paul J. Atzberger, Math, UCSB. Research areas include development of new machine learning methods, computational methods, and applications in the sciences and engineering. A particular emphasis is on novel ways to incorporate prior scientific knowledge, such as physical principles, into learning frameworks and methods.

The collaborative project involves interactions with groups at the Department of Energy (DOE) national laboratories and collaborating universities. More information about this position and research can be found at the job link below, and on Paul Atzberger’s website www.atzberger.org/.

JOB LINK:
recruit.ap.ucsb.edu/apply/JPF01322

POSITION: Postdoctoral Scholar In Scientific Computation and Machine Learning At The University of California, Santa Barbara (Research Group of Paul J. Atzberger)

DESCRIPTION
A postdoctoral position in the area of scientific computation and machine learning is available in the research group of Professor Paul J. Atzberger in the Department of Mathematics and Mechanical Engineering at the University of California Santa Barbara. The position involves collaborative research on projects including the development of new data-driven computational methods, numerical analysis, and large-scale scientific simulation. The projects also provide potential opportunities to be involved in activities at the national laboratories.

ADDITIONAL INFORMATION
Postdoctoral appointments are full-time training programs of advanced academic preparation and research training under the mentorship of a faculty member.

Basic Qualifications: PhD Degree in Mathematics, Computing Theory or a related discipline at the time of application.

Additional Qualifications: 1-2 years research experience or training related to mathematics and scientific computation.

Preferred Qualifications: Prior experience would be viewed especially favorably in the areas of large-scale scientific computation, stochastic analysis, machine learning/data-driven methods, and/or statistical mechanics, but are not strictly required.

The initial appointment will be for one (1) year with a possible two (2) year reappointment if mutually agreeable.

—————————————————-

Most recent SMB Digest’s What’s New in The Queue – Tuesday, Wednesday, Thursday, Friday

Comments are closed