What are the responsibilities and job description for the Postdoc/Research Scientist position at Laboratory for Research in Complex Systems?
Postdoc/Research-Scientist Positions in "Information and Complexity in Artificial Intelligence and Synthetic Biology"
Filled
Partially filled, extended to Sept. 1, 2020
Expires July 1, 2020
The Laboratory for Research in Complex Systems (LRC) is recruiting up to 3 independent, self-motivated physicists/computer-scientists/biologists based in Colombia to work as Postdocs/Research-Scientists on exploring information dynamics, origins of complexity and cognition in biological systems and biologically-inspired artificial intelligence. Work will be supervised by Dr. John Realpe-Gómez and Dr. Shailesh Date.
Candidates can potentially benefit from the LRC’s ongoing collaborations with other teams from Stanford, Caltech, JGI, NASA/JPL, and other institutes.
Location
The first year of work is to be performed in Colombia; afterwards, the option to join the LRC in the USA can be considered.
Project Description
The project aims at better understanding the drivers underlying the origin and evolution of complexity and cognition in biological systems, how biological systems can apparently reverse the tendency of entropy to increase and become information engines, how non-genomic information is transmitted between generations, and how learning and evolution interact. Insights obtained from this research will be used to explore novel biologically-inspired artificial intelligence algorithms. Quantitative models will be experimentally tested using bacterial model systems, with the aim of synthetically pushing biological systems towards new behaviors and functions.
Desired Qualifications
Candidates should have obtained a Ph.D. degree in the natural/quantitative sciences with emphasis on either modeling biological systems or artificial intelligence/machine learning. Strong computational and quantitative skills are required. Knowledge of evolutionary biology or cognitive science and of tools/platforms commonly used in artificial intelligence and bioinformatics will be considered a big plus. In all instances, candidates should demonstrate the ability to communicate and work with physicists and biologists to test predictive models.
Salary
Competitive and commensurate with experience.
To Apply
Please send a copy of your recent CV to the email below, with the subject line “Complexity Scientist”. Your CV should contain the following sections (in particular order): area of work, educational background, publications, awards and achievements, details of the doctoral project, and names and contact information for 3 referees. Referees will be contacted only for shortlisted candidates. Your help in complying with formatting guidelines is greatly appreciated; it will allow identification of suitable candidates quickly and efficiently.
Email address for sending CV
john.realpe AT lrc.systems
The candidate is expected to start work before June 2020.
The LRC welcomes all candidates regardless of race, religion, gender, or sexual orientation, and places a very high emphasis on diversity. Women and under-represented minorities are highly encouraged to apply.
Filled
Partially filled, extended to Sept. 1, 2020
Expires July 1, 2020
The Laboratory for Research in Complex Systems (LRC) is recruiting up to 3 independent, self-motivated physicists/computer-scientists/biologists based in Colombia to work as Postdocs/Research-Scientists on exploring information dynamics, origins of complexity and cognition in biological systems and biologically-inspired artificial intelligence. Work will be supervised by Dr. John Realpe-Gómez and Dr. Shailesh Date.
Candidates can potentially benefit from the LRC’s ongoing collaborations with other teams from Stanford, Caltech, JGI, NASA/JPL, and other institutes.
Location
The first year of work is to be performed in Colombia; afterwards, the option to join the LRC in the USA can be considered.
Project Description
The project aims at better understanding the drivers underlying the origin and evolution of complexity and cognition in biological systems, how biological systems can apparently reverse the tendency of entropy to increase and become information engines, how non-genomic information is transmitted between generations, and how learning and evolution interact. Insights obtained from this research will be used to explore novel biologically-inspired artificial intelligence algorithms. Quantitative models will be experimentally tested using bacterial model systems, with the aim of synthetically pushing biological systems towards new behaviors and functions.
Desired Qualifications
Candidates should have obtained a Ph.D. degree in the natural/quantitative sciences with emphasis on either modeling biological systems or artificial intelligence/machine learning. Strong computational and quantitative skills are required. Knowledge of evolutionary biology or cognitive science and of tools/platforms commonly used in artificial intelligence and bioinformatics will be considered a big plus. In all instances, candidates should demonstrate the ability to communicate and work with physicists and biologists to test predictive models.
Salary
Competitive and commensurate with experience.
To Apply
Please send a copy of your recent CV to the email below, with the subject line “Complexity Scientist”. Your CV should contain the following sections (in particular order): area of work, educational background, publications, awards and achievements, details of the doctoral project, and names and contact information for 3 referees. Referees will be contacted only for shortlisted candidates. Your help in complying with formatting guidelines is greatly appreciated; it will allow identification of suitable candidates quickly and efficiently.
Email address for sending CV
john.realpe AT lrc.systems
The candidate is expected to start work before June 2020.
The LRC welcomes all candidates regardless of race, religion, gender, or sexual orientation, and places a very high emphasis on diversity. Women and under-represented minorities are highly encouraged to apply.