Dr. John H. Maddocks
Professor, EPFL Lausanne, Switzerland, Laboratory for Computation & Visualization in Mathematics & Mechanics
Lab Website
The cgDNA sequence-dependent coarse-grain model of dsDNA: Bridging the scales from Molecular Dynamics to Bioinformatics
Tuesday, Oct. 29, 3:15 PM, MCB 102
Abstract: The cgDNA+ coarse-grain model of DNA (lcvmwww.epfl.ch/research/cgDNA/) can now accurately predict the sequence-dependent statistical mechanics properties, for example shape and stiffness (or equivalently first and second moments of the equilibrium distributions), of double-stranded DNA fragments of arbitrary sequence. At scales of tens of base pairs these predictions can be compared with Molecular Dynamics simulations and they agree very well. However the efficiency of the cgDNA+ model allows genome length scales to be scanned in order to identify mechanically exceptional sequence fragments, including in an epigenetically modified sequence alphabet.
Monday, October 28, 2019
Monday, October 21, 2019
Post-doc and Bioinformatician positions available in NYC
The Bunyavanich Lab welcomes talented, self-motivated individuals who can fulfill the responsibilities and requirements below to apply for positions in our lab at the Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY. The successful applicant will be part of an interdisciplinary team led by Dr. Supinda Bunyavanich that applies computational analysis and bioinformatics to interpret multi-scale data generated from subjects with asthma and allergic diseases. Our researchers receive generous packages, including robust salaries and a wealth of opportunities to participate in academic activities here at the Institute for Data Science and Genomic Technology and more broadly at regional and national workshops and conferences. We are located in the heart of Manhattan, and Mount Sinai is one of the oldest and largest teaching hospitals in the US.
Responsibilities:
• Analyze high-throughput sequence data.
• Develop and implement methods to analyze these data.
• Maintain large datasets linked to clinical data.
• Communicate progress with PI regularly and contribute to the success of the research team.
• Develop and maintain productive collaborations within Mount Sinai and with outside researchers in academia and industry.
• Publish and present novel research findings in academic journals and conferences
• Some supervision of trainees and technical staff may also be required.
Requirements:
• Degree in bioinformatics, computer science, computational biology, genomics, or a related field.
• Outstanding programming skills in R, Python, and Unix shell scripting.
• Excellent track record of analyzing sequence data. Experience with clinical cohorts and microbiome analysis a plus.
• Demonstrated knowledge of statistics and statistical genetics. Familiarity with genomic data tools, repositories, and databases.
• Strong attention to detail and solid analytical skills.
• Ability to work hard and independently while contributing to the team effort and adhering to deadlines.
• Excellent oral and written communication skills with track record of productive collaborations.
• Demonstrated ability to work concurrently on several projects, and good understanding of analytic complexities to do independent research as well as assist other researchers.
The Institute for Data Science and Genomic Technology at the Icahn School of Medicine at Mount Sinai seeks to comprehensively integrate the digital universe of information into research, training, and patient care and to develop programs that advance the future of healthcare and data science.
Interested and qualified candidates should submit a CV and detailed letter of interest to Dr. Supinda Bunyavanich (Supinda.Bunyavanich at mssm.edu).
Responsibilities:
• Analyze high-throughput sequence data.
• Develop and implement methods to analyze these data.
• Maintain large datasets linked to clinical data.
• Communicate progress with PI regularly and contribute to the success of the research team.
• Develop and maintain productive collaborations within Mount Sinai and with outside researchers in academia and industry.
• Publish and present novel research findings in academic journals and conferences
• Some supervision of trainees and technical staff may also be required.
Requirements:
• Degree in bioinformatics, computer science, computational biology, genomics, or a related field.
• Outstanding programming skills in R, Python, and Unix shell scripting.
• Excellent track record of analyzing sequence data. Experience with clinical cohorts and microbiome analysis a plus.
• Demonstrated knowledge of statistics and statistical genetics. Familiarity with genomic data tools, repositories, and databases.
• Strong attention to detail and solid analytical skills.
• Ability to work hard and independently while contributing to the team effort and adhering to deadlines.
• Excellent oral and written communication skills with track record of productive collaborations.
• Demonstrated ability to work concurrently on several projects, and good understanding of analytic complexities to do independent research as well as assist other researchers.
The Institute for Data Science and Genomic Technology at the Icahn School of Medicine at Mount Sinai seeks to comprehensively integrate the digital universe of information into research, training, and patient care and to develop programs that advance the future of healthcare and data science.
Interested and qualified candidates should submit a CV and detailed letter of interest to Dr. Supinda Bunyavanich (Supinda.Bunyavanich at mssm.edu).
Research and Fellowships Week
Research and Fellowships Week is an annual event hosted by USC’s Academic Honors and Fellowships, the Graduate School and the Office of Postdoctoral Affairs. The goal of the week is to discuss opportunities that support research, graduate study, language learning, teaching and internships within the U.S. and abroad. The sessions are open to students of all academic levels, staff and faculty, attendees participate in interactive panels and workshops to explore USC programs and external post-graduate possibilities.
The following activities are geared towards graduate students:
Monday, November 4
Fellowships for PhD Students in Health Fields, 12:00-12:50pm, HSC Norris Medical Library, West Conference Room
“The Novelty Moves”: How to Pitch Your Research to Potential Funders….and Beyond, 1:00-1:50pm, Doheny Library (DML) 241
Tuesday, November 5
Fellowships for International PhD Students, 1:00-1:50pm, Doheny Library (DML) 241
USC Advanced PhD Fellowships, 2:00-2:50pm, Doheny Library (DML) 241
Wednesday, November 6
Research with Impact: Data and Bibliometrics, 2:00-2:50pm, Doheny Library (DML) 241
Thursday, November 7
Focus on the NSF Graduate Research Fellowship Program, 1:00-1:50pm, Doheny Library (DML) 241
Friday, November 8
Fulbright U.S. Student Program,11:00-11:50am, Hedco Neurosciences Building (HNB 100)
More information about Research and Fellowships Week is available here: https://ahf.usc.edu/events/rfw/
The following activities are geared towards graduate students:
Monday, November 4
Fellowships for PhD Students in Health Fields, 12:00-12:50pm, HSC Norris Medical Library, West Conference Room
“The Novelty Moves”: How to Pitch Your Research to Potential Funders….and Beyond, 1:00-1:50pm, Doheny Library (DML) 241
Tuesday, November 5
Fellowships for International PhD Students, 1:00-1:50pm, Doheny Library (DML) 241
USC Advanced PhD Fellowships, 2:00-2:50pm, Doheny Library (DML) 241
Wednesday, November 6
Research with Impact: Data and Bibliometrics, 2:00-2:50pm, Doheny Library (DML) 241
Thursday, November 7
Focus on the NSF Graduate Research Fellowship Program, 1:00-1:50pm, Doheny Library (DML) 241
Friday, November 8
Fulbright U.S. Student Program,11:00-11:50am, Hedco Neurosciences Building (HNB 100)
More information about Research and Fellowships Week is available here: https://ahf.usc.edu/events/rfw/
QCB Colloquium | Dr. Yinglei Lai
Dr. Yinglei Lai
Professor, The George Washington University, Dept. of Statistics
Professional Website
Assessing the discovery reproducibility from a large-scale association analysis
Wednesday, Oct. 23, 3 PM, RRI 421
Abstract: Reproducibility plays essential roles in scientific research. Magnetic Resonance Imaging (MRI) and genomic/proteomic high-throughput technologies have been widely used in brain and health research. The Dice Similarity Coefficient (DSC) has been commonly used for assessing the reproducibility of discoveries in a large-scale association analysis. However, in the current assessment of reproducibility, there is a lack of efficiency in the use of all available samples. More importantly, there is a lack of consistency with the reported discoveries identified based on all available samples. We have developed a probabilistic framework to assess discovery reproducibility based on all available samples. In our results, we demonstrated the usefulness of our approach and its advantages over DSC. We identified the minimal sample size required to achieve a given reproducibility rate, which provides an informative guidance for planning large-scale association studies.
Professor, The George Washington University, Dept. of Statistics
Professional Website
Assessing the discovery reproducibility from a large-scale association analysis
Wednesday, Oct. 23, 3 PM, RRI 421
Abstract: Reproducibility plays essential roles in scientific research. Magnetic Resonance Imaging (MRI) and genomic/proteomic high-throughput technologies have been widely used in brain and health research. The Dice Similarity Coefficient (DSC) has been commonly used for assessing the reproducibility of discoveries in a large-scale association analysis. However, in the current assessment of reproducibility, there is a lack of efficiency in the use of all available samples. More importantly, there is a lack of consistency with the reported discoveries identified based on all available samples. We have developed a probabilistic framework to assess discovery reproducibility based on all available samples. In our results, we demonstrated the usefulness of our approach and its advantages over DSC. We identified the minimal sample size required to achieve a given reproducibility rate, which provides an informative guidance for planning large-scale association studies.
QCB Colloquium | Dr. Siavash Mirarab
Dr. Siavash Mirarab
Assistant Professor, UCSD, Dept. of Electrical & Computer Engineering
Faculty Profile
Assembly-free and alignment-free sample identification and phylogenetic placement using genome skims
Thursday, Oct. 24, 2 PM, RRI 101
Abstract: The ability to inexpensively describe taxonomic diversity is critical in this era of rapid climate and biodiversity changes. The recent genome-skimming approach extends current barcoding practices beyond short markers by applying low-pass sequencing and recovering whole organelle genomes computationally. This approach discards the nuclear DNA, which constitutes the vast majority of the data. In contrast, we suggest using all unassembled reads. We introduce an assembly-free and alignment-free tool, Skmer, to compute genomic distances between the query and reference genome skims. Skmer is based on a fast computation of Jaccard index and appropriate corrections for lack of coverage. Skmer shows excellent accuracy in estimating distances and identifying the closest match in reference datasets. When paired with our new phylogenetic placement tool, APPLES, it can perform distance-based phylogenetics.
Assistant Professor, UCSD, Dept. of Electrical & Computer Engineering
Faculty Profile
Assembly-free and alignment-free sample identification and phylogenetic placement using genome skims
Thursday, Oct. 24, 2 PM, RRI 101
Abstract: The ability to inexpensively describe taxonomic diversity is critical in this era of rapid climate and biodiversity changes. The recent genome-skimming approach extends current barcoding practices beyond short markers by applying low-pass sequencing and recovering whole organelle genomes computationally. This approach discards the nuclear DNA, which constitutes the vast majority of the data. In contrast, we suggest using all unassembled reads. We introduce an assembly-free and alignment-free tool, Skmer, to compute genomic distances between the query and reference genome skims. Skmer is based on a fast computation of Jaccard index and appropriate corrections for lack of coverage. Skmer shows excellent accuracy in estimating distances and identifying the closest match in reference datasets. When paired with our new phylogenetic placement tool, APPLES, it can perform distance-based phylogenetics.
Monday, October 14, 2019
Need lecturer to teach Landscape Ecology
We are looking for a lecturer to teach our Landscape Ecology class next semester (Spring, 2020). It is upper division class taken by a mix of upperclassmen and Master’s students.
Anyone (grad students, postdocs, junior scientists) who might be interested in teaching such a class should contact me directly.
I would be happy to discuss pay and schedule with any interested parties.
Thank you.
H. K. Choi, Ph.D.
Associate Professor and Chair, Department of Biology
California State University, Dominguez Hills
1000 East Victoria Street, Carson CA 90747
Tel: (310) 243-3382 (Office)
Fax: (310) 243-2350
Email: hchoi@csudh.edu
Anyone (grad students, postdocs, junior scientists) who might be interested in teaching such a class should contact me directly.
I would be happy to discuss pay and schedule with any interested parties.
Thank you.
H. K. Choi, Ph.D.
Associate Professor and Chair, Department of Biology
California State University, Dominguez Hills
1000 East Victoria Street, Carson CA 90747
Tel: (310) 243-3382 (Office)
Fax: (310) 243-2350
Email: hchoi@csudh.edu
Stanford Science Fellows Postdoctoral Training Program
Stanford University is launching a new fellows program for postdoctoral researchers. I welcome your help in encouraging promising candidates to apply. The deadline for application is November 1, 2019.
The Stanford Science Fellows program is focused on incubating new directions in foundational scientific research through an interdisciplinary community of exceptional postdoctoral scholars from around the world who are driven by a sense of wonder about the natural world. The goals of this new postdoctoral training program are:
• To deepen our understanding of the natural world by advancing and bridging disciplines in the physical, mathematical, and life sciences.
• To provide opportunities for exceptionally qualified early-career scientists to develop academic leadership skills focused on fostering scientific discovery, acquire interdisciplinary approaches to foundational scientific research, and broaden science communication skills.
• To recognize and support scholars who bring a diversity of perspectives, identities, and backgrounds, including those from groups who are underrepresented in the sciences.
• To build community at Stanford around frontier research challenges.
• To provide flexibility and resources for bold, independent thinkers to pursue their own scientific research visions.
Stanford Science Fellowships are intended for exceptional early-career postdoctoral scholars who have recently been awarded their PhD or will be awarded their PhD by the start of the fellowship and who want to pursue research and training in any natural science discipline. International scholars are welcome to apply.
Between 4 to 8 fellows will be selected to start appointments between July 1 and September 1, 2020. Prospective fellows will be considered by a committee of Stanford faculty from departments and schools across the natural sciences at Stanford. An explicit goal of these selection committees, in addition to selecting candidates with tremendous intellectual strength, will be the enhancement of demographic and intellectual diversity in the scientific community at Stanford.
Learn more about the Stanford Science Fellows and the application at stanfordsciencefellows.stanford.edu.
Stanford Science Fellow appointments will be for three-year terms. Fellows will receive an annual stipend of $83,000 per year, benefits, and up to $8,000 per year supplemental funds to support research and professional development.
Interested applicants must apply online (stanfordsciencefellows.stanford.edu) by November 1, 2019.
Inquiries may be directed to stanfordsciencefellows@stanford.edu.
The Stanford Science Fellows program is focused on incubating new directions in foundational scientific research through an interdisciplinary community of exceptional postdoctoral scholars from around the world who are driven by a sense of wonder about the natural world. The goals of this new postdoctoral training program are:
• To deepen our understanding of the natural world by advancing and bridging disciplines in the physical, mathematical, and life sciences.
• To provide opportunities for exceptionally qualified early-career scientists to develop academic leadership skills focused on fostering scientific discovery, acquire interdisciplinary approaches to foundational scientific research, and broaden science communication skills.
• To recognize and support scholars who bring a diversity of perspectives, identities, and backgrounds, including those from groups who are underrepresented in the sciences.
• To build community at Stanford around frontier research challenges.
• To provide flexibility and resources for bold, independent thinkers to pursue their own scientific research visions.
Stanford Science Fellowships are intended for exceptional early-career postdoctoral scholars who have recently been awarded their PhD or will be awarded their PhD by the start of the fellowship and who want to pursue research and training in any natural science discipline. International scholars are welcome to apply.
Between 4 to 8 fellows will be selected to start appointments between July 1 and September 1, 2020. Prospective fellows will be considered by a committee of Stanford faculty from departments and schools across the natural sciences at Stanford. An explicit goal of these selection committees, in addition to selecting candidates with tremendous intellectual strength, will be the enhancement of demographic and intellectual diversity in the scientific community at Stanford.
Learn more about the Stanford Science Fellows and the application at stanfordsciencefellows.stanford.edu.
Stanford Science Fellow appointments will be for three-year terms. Fellows will receive an annual stipend of $83,000 per year, benefits, and up to $8,000 per year supplemental funds to support research and professional development.
Interested applicants must apply online (stanfordsciencefellows.stanford.edu) by November 1, 2019.
Inquiries may be directed to stanfordsciencefellows@stanford.edu.
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