The International Society for Bayesian Analysis
Postdoctoral Scholar – Stochastic Modeling for Renewable Energy at University
The International Society for Bayesian Analysis, Santa Barbara, California, us, 93190
Postdoctoral Scholar – Stochastic Modeling for Renewable Energy at University of California, Santa Barbara
Jul 10, 2020A postdoctoral position in stochastic modeling is available in the research group of Professor Mike Ludkovski in the Department of Statistics & Applied Probability at the University of California Santa Barbara. The position involves collaborative research on the ARPA-E project ‘Stochastic Models, Indices & Optimization Algorithms for Pricing & Hedging Reliability Risks in Modern Power Grids’ to develop data-driven large-scale stochastic simulations of renewable energy generation. The candidate should have expertise in at least one of the following areas: stochastic simulation, spatio-temporal statistics, energy finance, or machine learning. The projects also provide potential opportunities to be involved in activities at the national laboratories.Postdoctoral appointments are full-time training programs of advanced academic preparation and research training under the mentorship of a faculty member.Basic Qualifications:
To apply for this position, applicants must have completed all requirements for a Ph.D. (or equivalent) except the dissertation in Statistics, Financial Engineering, Applied Mathematics, or a related discipline at the time of application.Additional Qualifications:
Ph.D. conferred by the anticipated appointment start date.Preferred Qualifications:
Prior experience in any of statistical machine learning, modeling of renewable resources (solar, wind, temperature), stochastic analysis, and mathematical finance. Strong programming skills, including knowledge of R and Python, and expertise in scientific computation are desirable.The initial appointment will be for one (1) year with a possible two (2) year reappointment based on the continuation of funding and performance.Application Process:
For primary consideration, interested candidates should use the following link to submit a completed application including an up-to-date curriculum vitae, cover letter, statement of research, and three letters of reference by July 15, 2020. We strongly encourage applicants to submit an optional Statement of Contributions to Diversity. These statements, if submitted, will be reviewed for evidence of teaching, research, professional, and/or public service contributions that promote diversity and equal opportunity, such as effective strategies used for the educational advancement, retention, and mentoring of students in various under-represented groups.The anticipated start date is October 1, 2020 but no later than January 1, 2021.The department is especially interested in candidates who can contribute to the diversity & excellence of the academic community through teaching, research, and service.The University of California is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law.
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Jul 10, 2020A postdoctoral position in stochastic modeling is available in the research group of Professor Mike Ludkovski in the Department of Statistics & Applied Probability at the University of California Santa Barbara. The position involves collaborative research on the ARPA-E project ‘Stochastic Models, Indices & Optimization Algorithms for Pricing & Hedging Reliability Risks in Modern Power Grids’ to develop data-driven large-scale stochastic simulations of renewable energy generation. The candidate should have expertise in at least one of the following areas: stochastic simulation, spatio-temporal statistics, energy finance, or machine learning. The projects also provide potential opportunities to be involved in activities at the national laboratories.Postdoctoral appointments are full-time training programs of advanced academic preparation and research training under the mentorship of a faculty member.Basic Qualifications:
To apply for this position, applicants must have completed all requirements for a Ph.D. (or equivalent) except the dissertation in Statistics, Financial Engineering, Applied Mathematics, or a related discipline at the time of application.Additional Qualifications:
Ph.D. conferred by the anticipated appointment start date.Preferred Qualifications:
Prior experience in any of statistical machine learning, modeling of renewable resources (solar, wind, temperature), stochastic analysis, and mathematical finance. Strong programming skills, including knowledge of R and Python, and expertise in scientific computation are desirable.The initial appointment will be for one (1) year with a possible two (2) year reappointment based on the continuation of funding and performance.Application Process:
For primary consideration, interested candidates should use the following link to submit a completed application including an up-to-date curriculum vitae, cover letter, statement of research, and three letters of reference by July 15, 2020. We strongly encourage applicants to submit an optional Statement of Contributions to Diversity. These statements, if submitted, will be reviewed for evidence of teaching, research, professional, and/or public service contributions that promote diversity and equal opportunity, such as effective strategies used for the educational advancement, retention, and mentoring of students in various under-represented groups.The anticipated start date is October 1, 2020 but no later than January 1, 2021.The department is especially interested in candidates who can contribute to the diversity & excellence of the academic community through teaching, research, and service.The University of California is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law.
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