We are always looking for talented individuals to work with us!
Postdocs: postdoc@choderalab.org
Technicians: apply@choderalab.org
Prospective graduate students: You can join the lab from any of these graduate programs:
OPEN POSTDOCTORAL POSITIONS
BAYER SPONSORED POSTDOCTORAL FELLOW (NEW YORK CITY)
We are seeking a talented postdoctoral fellow to work at the interface of structure-informed machine learning and alchemical free energy calculations as part of an exciting new collaboration between Prof. Dr. Andrea Volkamer and BIH Einstein Visiting Fellow Prof. Dr. John Chodera. This project seeks to develop an integrated framework for utilizing both state-of-the-art machine learning approaches and free energy calculations to exploit structural data, assay data, and scalable computing resources to predict the impact of point mutations on the binding of small molecule kinase inhibitors, with a goal toward expanding the utility of clinical kinase inhibitors. Throughout this project, we embrace open science, open source software, and open access scientific communication.
The position is part of a collaboration with Bayer AG, Germany, pending final confirmation of funding.
The postdoc will be embedded in the Chodera lab at the Memorial Sloan Kettering Cancer Center in New York City, and make a few visits to the Volkamer group, at the Charité in Berlin. The postdoc will be jointly supervised by John Chodera and Andrea Volkamer, with Andrea Volkamer making several visits to MSKCC per year under the auspices of the BIH Einstein Visiting Fellowship.
The PostDoc will work in close collaboration with Bayer AG, Germany, the sponsor of this position. The main goal of the project will be to apply physical modeling and machine learning methods to assess the impact of clinical mutation on small molecule kinase inhibitor binding, as well as the development of methods and infrastructure to support these applications. Work will focus on the development and use of an open source framework for integrating absolute and relative alchemical free energy calculations with structure-informed machine learning approaches to develop a general framework for the susceptibility of inhibitor binding affinities to clinical mutations. We will utilize our open source GPU-accelerated Python toolkits (such as openmmtools, yank, and perses) for protein-ligand absolute and relative alchemical free energy calculations (built on OpenMM) within robust modeling and prediction workflows. This is an exciting opportunity to work at the intersection of physical modeling, machine learning, and drug discovery as a stepping stone to a career in industry or academia. This position will primarily involve contributing to the development of open source software and publication on open, public datasets, with the exciting opportunity of also working in close collaboration with Bayer on industry datasets.
Starting date: We are seeking candidates to start as soon as possible (2 years)
Salary: 25% above MSKCC Research Fellow salary scale (starts at $64,875 for 0 years postdoctoral experience); eligible for subsidized MSK housing in NYC
Desired qualifications:
Experience with biomolecular simulations and/or machine learning for drug discovery
Experienced with the Python programming language
Exposure to modern open source software development practices (GitHub, unit tests, continuous integration)
Good multidisciplinary team working and communication skills
Bonus qualifications:
Experience with high-performance computing clusters and/or cloud computing
Experience with OpenMM or machine learning frameworks such as TensorFlow
Experience with the theory and practice of small molecule alchemical free energy calculations
Experience with cheminformatics, computer-aided drug discovery and/or kinase inhibition
About the Chodera lab at the Memorial Sloan Kettering Cancer Center (MSKCC) in NYC:
The Chodera lab focuses on redesigning the way we develop small molecules for chemical biology and drug discovery and bringing rigorous atomistic modeling into the high-throughput biology and genomics era. By combining novel algorithmic advances to achieve orders-of-magnitude efficiency gains with powerful but inexpensive GPU hardware and distributed computing technologies, the lab is developing a new generation of tools and open source software packages for predicting small molecule binding affinities, designing small molecules with desired properties, quantifying drug sensitivity or resistance of clinical mutations, and understanding the detailed structural mechanisms underlying oncogenic mutations. As a core member of the Folding@home Consortium, the lab harnesses the computing power of hundreds of thousands of volunteers around the world to study functional implications of mutations and new opportunities for therapeutic design against cancer targets. Using automated biophysical measurements, the lab collects new experimental data targeted to advance the quantitative accuracy of our methodologies, and gather new insight into drug susceptibility and resistance in kinases and other cancer targets. To do this, the lab makes extensive use of scalable Bayesian statistical inference methods and information theoretic principles for designing experiments and quantifying error. They are passionate about open science, disseminating software engineering best practices, and maximizing research reproducibility in all aspects of their work.
Find out more at the Chodera lab webpage: http://choderalab.org
About the Volkamer lab at the Charité Universitätsmedizin, Berlin
We are a young, energetic and interdisciplinary research group with focus on development of structure-based methods for computer-aided drug design and risk assessment. The research field of the group englobes diverse aspects of structural bioinformatics (e.g. protein active site prediction, analysis and comparison, pharmacophores, (off-)target prediction, docking and inhibitor design and optimization) and cheminformatics (e.g. machine learning for activity and toxicity prediction). We mainly focus on method development and their application in the computer-aided drug discovery (CADD) context, i.e. for for rational design of more selective and less toxic substances, especially in the context of cancer research.
Find out more at our webpage: http://volkamerlab.org/
How to apply: Interested candidates are invited to send a pre-application to einstein@choderalab.org with the subject line “Postdoc application: NYC” that includes:
a cover letter explaining your motivation, background, and qualifications for the position
a detailed Curriculum Vitae (including a list of publications)
contact information of two references
Please contact us by 30 Nov 2019 for fullest consideration.
BAYER SPONSORED POSTDOCTORAL FELLOW (BERLIN)
We are seeking a talented postdoctoral fellow to work at the interface of structure-informed machine learning and alchemical free energy calculations as part of an exciting new collaboration between Prof. Dr. Andrea Volkamer and BIH Einstein Visiting Fellow Prof. Dr. John Chodera. This project seeks to develop an integrated framework for utilizing both state-of-the-art machine learning approaches and free energy calculations to exploit structural data, assay data, and scalable computing resources to predict the impact of point mutations on the binding of small molecule kinase inhibitors, with a goal toward expanding the utility of clinical kinase inhibitors. Throughout this project, we embrace open science, open source software, and open access scientific communication.
The position is part of a collaboration with Bayer AG, Germany, pending final confirmation of funding.
The postdoc will be embedded in the Volkamer group, situated within the exciting research environment of the Charité in Berlin, and make a few extended visits to the Chodera lab at the Memorial Sloan Kettering Cancer Center in New York City. The postdoc will be jointly supervised by Andrea Volkamer and John Chodera, with John Chodera making several extended visits to the Charité per year under the auspices of the BIH Einstein Visiting Fellowship.
The PostDoc will work in close collaboration with Bayer AG, Germany, the sponsor of this position. The main goal of the project will be to apply physical modeling and machine learning methods to assess the impact of clinical mutation on small molecule kinase inhibitor binding, as well as the development of methods and infrastructure to support these applications. Work will focus on the development and use of an open source framework for integrating absolute and relative alchemical free energy calculations with structure-informed machine learning approaches to develop a general framework for the susceptibility of inhibitor binding affinities to clinical mutations. We will utilize our open source GPU-accelerated Python toolkits (such as openmmtools, yank, and perses) for protein-ligand absolute and relative alchemical free energy calculations (built on OpenMM) within robust modeling and prediction workflows. This is an exciting opportunity to work at the intersection of physical modeling, machine learning, and drug discovery as a stepping stone to a career in industry or academia. This position will primarily involve contributing to the development of open source software and publication on open, public datasets, with the exciting opportunity of also working in close collaboration with Bayer on industry datasets.
Starting date: We are seeking candidates to start as soon as possible (2 years)
Salary: TV-L E13, 100%
Desired qualifications:
Experience with biomolecular simulations and/or machine learning for drug discovery
Experienced with the Python programming language
Exposure to modern open source software development practices (GitHub, unit tests, continuous integration)
Good multidisciplinary team working and communication skills
Bonus qualifications:
Experience with high-performance computing clusters and/or cloud computing
Experience with OpenMM or machine learning frameworks such as TensorFlow
Experience with the theory and practice of small molecule alchemical free energy calculations
Experience with cheminformatics, computer-aided drug discovery and/or kinase inhibition
About the Chodera lab at the Memorial Sloan Kettering Cancer Center (MSKCC) in NYC:
The Chodera lab focuses on redesigning the way we develop small molecules for chemical biology and drug discovery and bringing rigorous atomistic modeling into the high-throughput biology and genomics era. By combining novel algorithmic advances to achieve orders-of-magnitude efficiency gains with powerful but inexpensive GPU hardware and distributed computing technologies, the lab is developing a new generation of tools and open source software packages for predicting small molecule binding affinities, designing small molecules with desired properties, quantifying drug sensitivity or resistance of clinical mutations, and understanding the detailed structural mechanisms underlying oncogenic mutations. As a core member of the Folding@home Consortium, the lab harnesses the computing power of hundreds of thousands of volunteers around the world to study functional implications of mutations and new opportunities for therapeutic design against cancer targets. Using automated biophysical measurements, the lab collects new experimental data targeted to advance the quantitative accuracy of our methodologies, and gather new insight into drug susceptibility and resistance in kinases and other cancer targets. To do this, the lab makes extensive use of scalable Bayesian statistical inference methods and information theoretic principles for designing experiments and quantifying error. They are passionate about open science, disseminating software engineering best practices, and maximizing research reproducibility in all aspects of their work.
Find out more at the Chodera lab webpage: http://choderalab.org
About the Volkamer lab at the Charité Universitätsmedizin, Berlin
We are a young, energetic and interdisciplinary research group with focus on development of structure-based methods for computer-aided drug design and risk assessment. The research field of the group englobes diverse aspects of structural bioinformatics (e.g. protein active site prediction, analysis and comparison, pharmacophores, (off-)target prediction, docking and inhibitor design and optimization) and cheminformatics (e.g. machine learning for activity and toxicity prediction). We mainly focus on method development and their application in the computer-aided drug discovery (CADD) context, i.e. for for rational design of more selective and less toxic substances, especially in the context of cancer research.
Find out more at our webpage: http://volkamerlab.org/
How to apply: Interested candidates are invited to send a pre-application to einstein@choderalab.org with the subject line “Postdoc application” that includes:
a cover letter explaining your motivation, background, and qualifications for the position
a detailed Curriculum Vitae (including a list of publications)
contact information of two references
Please contact us by 30 Nov 2019 for fullest consideration.
HIGH PERFORMANCE COMPUTING POSTDOCTORAL FELLOW OR STAFF SCIENTIST
(BROOKHAVEN NATIONAL LAB)
In collaboration with Shantenu Jha (Brookhaven National Lab / Rutgers University), we are hiring a postdoc or staff scientist to work at the intersection of software development and high-performance computing, spearheading the development of software infrastructure for scalable alchemical free energy calculations and machine learning.
This position is at the interface of high-performance computing, machine learning and computational science (three year term position with an option for extension).
Essential Duties and Responsibilities:
Experience with High-Performance Computing --- principles, practice, programming and performance analysis
Research, design and implement novel methods and algorithms with focus on computational biology
Promote the research results through scholarly publications and presentations at leading conferences
Lead or participate in the development of related research proposals.
Promote collaborative research with interdisciplinary research team
Experience with Scientific Workflows and Resource Management
Develop High-Performance Machine Learning frameworks and libraries, and their integration with scientific applications
Required Knowledge, Skills, and Abilities:
PhD in Computer Science, Statistics, Applied Math or related discipline
Advanced working knowledge of machine learning and AI
Python and C programming experience
Experience working in multidisciplinary scientific collaboration
Track record of producing high quality software on schedule
Preferred Knowledge, Skills, and Abilities:
Appreciation for a range of scientific domains — bimolecular to climate
Experience working in inter-disciplinary teams
A degree in CS/CE in high-performance computing
Experience in system software design and implementation of scalable systems
About Brookhaven National Laboratory:
Brookhaven National Laboratory is a multipurpose research institution funded primarily by the U.S. Department of Energy’s Office of Science. Located on the center of Long Island, New York, Brookhaven Lab brings world-class facilities and expertise to the most exciting and important questions in basic and applied science—from the birth of our universe to the sustainable energy technology of tomorrow. We operate cutting-edge large-scale facilities for studies in physics, chemistry, biology, medicine, applied science, and a wide range of advanced technologies. The Laboratory's almost 3,000 scientists, engineers, and support staff are joined each year by more than 4,000 visiting researchers from around the world. Our award-winning history, including seven Nobel Prizes, stretches back to 1947, and we continue to unravel mysteries from the nanoscale to the cosmic scale, and everything in between. Brookhaven is operated and managed by Brookhaven Science Associates, which was founded by the Research Foundation for the State University of New York on behalf of Stony Brook University, and Battelle, a nonprofit applied science and technology organization.
Applications: Interested candidates should send a cover letter and CV to postdoc@choderalab.org with the subject line “BNL Postdoc Position” or “BNL Staff Scientist Position”
OPEN FORCE FIELD INITIATIVE - CONSORTIUM SPONSORED POSTDOC (FLEXIBLE LOCATION)
The Open Force Field Initiative is seeking a talented postdoctoral fellow to work on the development of modern toolkits and new methods for building next-generation molecular mechanics force fields. The position is supported by the Open Force Field Consortium, which is administered by the Molecular Sciences Software Institute (MolSSI) and funded by an alliance of pharmaceutical and biotech companies aiming to advance the state of force fields for biomolecular simulation and design through an open collaborative effort.
The Open Force Field Initiative is a multi-investigator collaboration that aims to build new, quantitatively accurate molecular mechanics force fields supported by a modern software infrastructure, built on principles of open source, open data, and open science. More about their scientific goals and plans can be found in the Executive Summary or Open Force Field Roadmap.
Desired qualifications:
Experience with the theory and practice of atomistic biomolecular simulation using molecular mechanics force fields
Comfortable with the Python programming language
Exposure to modern open source software development practices (GitHub, unit tests, continuous integration)
Good multidisciplinary teamwork and communication skills
Bonus qualifications (nice to have, but not required!):
Experience with force field parameter fitting for small organic molecules
Experience with quantum chemical calculations in general, and psi4 in particular
Experience with high-performance computing clusters and/or cloud computing
Knowledge of organic chemistry
Experience with the open-source GPU-accelerated molecular simulation Python library OpenMM
Experience with probabilistic programming languages (e.g. PyMC, tensorflow.Probability, Pyro) and/or machine learning frameworks like TensorFlow
Experience with cheminformatics or computer-aided drug discovery
Location: Choice of location is flexible, with potential sites including the participating investigator laboratories: Michael K. Gilson (UCSD, La Jolla, CA), John D. Chodera (MSKCC, New York City, NY), Lee-Ping Wang (UCD, Davis, CA), David L. Mobley (UCI, Irvine, CA), or Michael R. Shirts (University of Colorado, Boulder, CO).
Appointment: The initial appointment will be for one year, with the potential for extension to multiple years to focus on other aspects of force field science and engineering. Position is pending final approval of funding.
Open Force Field Initiative: To find out more about the Open Force Field Initiative, visit http://openforcefield.org.
Application:
Interested candidates should send an application to openforcefield@choderalab.org with the subject line “Postdoc application” that includes:
• a cover letter explaining your motivation, background, and qualifications for the position
• a detailed Curriculum Vitae (including a list of publications)
• contact information of two references