Research Fellow
3 days ago
The RF will be responsible to lead data science and AI research for a large multinational agriculture technology (AgriTech) research facility. The RF will work closely with leading academics in machine learning, statistics and data science from both Israel and Singapore. Position includes top-notch continued training in data science, and co-authorship on publications produced by the iSURF research team.
Specifically, the RF will be responsible for data collection from iSURF’s unique research facility, which includes a large array of computer-controlled plant sensor and cameras; preprocessing production system; real-time reinforcement learning-based plant environment control; development and testing of a statistical model for individual plant physiology. The project involves a large number of cutting-edge problems in signal processing, time series analysis, high-dimensional statistical inference, machine vision, machine learning and deep reinforcement learning - all with significant potential impact, both academic and industrial, on the emerging field of precision urban agriculture.
Key Responsibilities:
- Perform data science and AI research- Ensure compliance with health and safety in all aspects of the research work- Support supervision of junior research staff, undergraduate and graduate student projects- Coordinate collaborative work within multidisciplinary team and external collaborators- Generate periodic progress report as required by funding agency- Disseminate research results in reputable peer-reviewed journals and/or international conference.
Job Requirements:
- PhD in data science, statistics, computer science, mathematics, physics or electrical engineering.- Software development experience in Python on cloud unix environment- Hands-on experience in data analysis, statistical inference and predictive machine learning in Python and R- Relational and no-sql data management and data engineering experience- Proficiency in data preprocessing and visualization- Familiarity with at least several of the following, and willingness to learn the rest: classical signal processing, linear filters, time series analysis, bootstrap methods, survival analysis, generalized linear models, regularization methods, analysis of variance and related methods, categorical data analysis, experiment design, nonlinear dimensionality reduction, boosting methods, control theory, wavelet methods in signal processing, model selection methods, high-dimensional covariance estimation, deep net image processing and image analysis, autoencoders, causal machine learning, transfer learning, reinforcement learning.- Self-starting, motivated, curious and creative. Fast learner and careful thinker.- Interest in research related to food security and the nexus of AI and plant science-
- Good communication and writing skills- Good coordination/project management skills- Able to take initiative to improve processes and troubleshoot problems- Able to organize well and adhere to deadlines
Hiring Institution: NTU
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