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Position: Research Data Scientist (Postdoc)
Institution: Haute École d'Ingénierie et de Gestion du Canton de Vaud, Predictive Layer
Location: Yverdon‐les‐Bains, Vaud, Switzerland
Duties: The researcher will participate to an exciting research project in collaboration with Predictive Layer, which is specialized into time series forecasting, using artificial intelligence and machine-learning to solve business problems. It processes its clients’ data with exogenous data to make near-term forecasts. The systems to be developed under the research projects are based on forecasting time series data. Fluctuations and trends of financial target signals result from strong dynamic interactions with the external environment whose underlying factors are constantly changing in their relative contribution
Requirements: PhD in mathematics (possibly related to data science and machine learning algorithms), eventually in statistics, theoretical physics or theoretical computer science with excellent academic and publication records; Very good knowledge of Python; Excellent writing and verbal communication skills, as well as presentation skills. Besides proficiency in English, creativity, innovative and independent thinking is a must
   
Text: HEIG-Vd has an opening for a Research Data Scientist (Postdoc) The researcher will participate to an exciting research project in collaboration with Predictive Layer, which is specialized into time series forecasting, using artificial intelligence and machine-learning to solve business problems. It processes its clients’ data with exogenous data to make near-term forecasts. The systems to be developed under the research projects are based on forecasting time series data. Fluctuations and trends of financial target signals result from strong dynamic interactions with the external environment whose underlying factors are constantly changing in their relative contribution. As trading strategies require to adapt to changing market conditions and scale with increasing allocation of computing resources under strict time execution constraints, new research and algorithms need to be developed to model dynamically the final structure of the deep neural network (DNN) - based predictive engines. Nowadays, Predictive Layer delivers solutions mainly based on the optimization of Ensemble Trees and static neural network architectures upon processing prior information from available time series datasets. The core algorithm needs to work on large set of financial data under strict time constraints. The novel DNN architecture will select information and shape dynamically compared to traditional methods in order to achieve high performances and stability over time and ensure sufficient model diversity in the final decision process. The efficient design of an adaptive neural network architecture is extremely challenging from both scientific and engineering point of views due to the inherent complexity and ill-conditioning of the problem, the combination of multiple models and the execution time constraints. These news methods will be implemented and tested in collaboration with Predictive Layer. Requirements • PhD in mathematics (possibly related to data science and machine learning algorithms), eventually in statistics, theoretical physics or theoretical computer science with excellent academic and publication records • Very good knowledge of Python • Excellent writing and verbal communication skills, as well as presentation skills. Besides proficiency in English, creativity, innovative and independent thinking is a must. He shows motivation to collaborate in an interdisciplinary international team, to participate in training programs, and is willing to travel to present his work to international conferences Activity: 100% More information on http://www.stephan-robert.ch/jobs and stephan.robert@heig-vd.ch Starting date: when available Contract: 18 months
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